Fail thresholding in a batch simulation farm network

ABSTRACT

A method and system for tracking frequently occurring fail events that are detected during testcase simulation of a simulation model within a batch simulation farm in which testcases are executed within respect to a simulation model on one or more simulation clients. In accordance with the method of the present invention, the instrumentation server receives fail event packets from one or more simulation clients. The fail event packets contains an aggregate of detected occurrences of a specified fail event. The instrumentation server monitors the rate of occurrence of the specified fail event from received fail event packets to detect an excess rate of occurrence of the specified fail event.

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] The present application is related to the following co-pendingU.S. patent application: U.S. patent application Ser. No. ______ (DocketNo. AUS920010960US1) filed on ______, titled “Maintaining Data IntegrityWithin A Distributed Simulation Environment”; U.S. patent applicationSer. No. ______ (Docket No. AUS920010962US1) filed on ______, titled“Centralized Disablement Of Instrumentation Events Within A BatchSimulation Farm Network”; U.S. patent application Ser. No. ______(DocketNo. AUS920010963US1) filed on ______, titled “Count Data Access In ADistributed Simulation Environment”; U.S. patent application Ser. No.______ (Docket No. AUS920000652US1) filed on ______, titled “TrackingCoverage Results In A Batch Simulation Farm Network”; U.S. patentapplication Ser. No. ______ (Docket No. AUS920000651US1) filed on______, titled “Non-Redundant Collection Of Harvest Events Within ABatch Simulation Farm Network”; and U.S. patent application Ser. No.______ (Docket No. AUS920010961US1) filed on ______, titled “AnnealingHarvest Testcase Collection Within A Batch Simulation Farm”. Theabove-mentioned patent applications are assigned to the assignee of thepresent invention and are incorporated herein by reference.

BACKGROUND OF THE INVENTION

[0002] 1. Technical Field

[0003] The present invention relates in general to designing andsimulating digital devices, modules and systems in a distributedsimulation environment. In particular, the present invention relates toa method and system that improve a distributed simulation environment toallow for efficient monitoring and utilization of instrumentation eventsembedded with a simulation model. More particularly, the presentinvention relates to method and system for tracking frequently occurringfail events that are detected during testcase simulation of a simulationmodel within a batch simulation farm wherein testcases are executedwithin respect to a simulation model on one or more simulation clients.

[0004] 2. Description of the Related Art

[0005] Verifying the logical correctness of a digital design anddebugging the design, if necessary, are very important steps in mostdigital design processes. Logic networks are tested either by actuallybuilding networks or by simulating networks on a computer. As logicnetworks become highly complex, it becomes necessary to simulate adesign before the design is actually built. This is especially true whenthe design is implemented as an integrated circuit, since thefabrication of integrated circuits requires considerable time andcorrection of mistakes is quite costly. The goal of digital designsimulation is the verification of the logical correctness of the design.

[0006] In a typical automated design process that is supported by aconventional electronic computer-aided design (ECAD) system, a designerenters a high-level description utilizing a hardware descriptionlanguage (HDL), such as VHDL, producing a representation of the variouscircuit blocks and their interconnections. The ECAD system compiles thedesign description into a format that is best suited for simulation. Asimulator is then utilized to verify the logical correctness of thedesign prior to developing a circuit layout.

[0007] A simulator is typically a software tool that operates on adigital representation, or simulation model of a circuit, and a list ofinput stimuli representing inputs of the digital system. A simulatorgenerates a numerical representation of the response of the circuitwhich may then either be viewed on the display screen as a list ofvalues or further interpreted, often by a separate software program, andpresented on the display screen in graphical form. The simulator may berun either on a general purpose computer or on another piece ofelectronic apparatus, typically attached to a general purpose computer,specially designed for simulation. Simulators that run entirely insoftware on a general purpose computer will hereinafter be referred toas “software simulators”. Simulators that are run with the assistance ofspecially designed electronic apparatus will hereinafter be referred toas “hardware simulators”.

[0008] Usually, software simulators perform a very large number ofcalculations and operate slowly from the user's point of view. In orderto optimize performance, the format of the simulation model is designedfor very efficient use by the simulator. Hardware simulators, by nature,require that the simulation model comprising the circuit description becommunicated in a specially designed format. In either case, atranslation from an HDL description to a simulation format, hereinafterreferred to as a simulation executable model, is required.

[0009] The complexity of modern digital circuits demands an enormousamount of resources dedicated to performing and processing simulation ofvarious simulation models. As a result, it is common to employ so-called“batch simulation farms” consisting of hundreds to thousands ofcomputers employing hardware and software simulators. These systems areusually connected to a shared network and run simulation jobs withrespect to one or more digital designs. The large numbers of computersperforming foreground or background simulation testing enables the largenumber of simulations required by modern designs to be performed in atimely manner.

[0010] A batch simulation farm often encompasses general-purposecomputers at geographically separated sites. For example, computers at alocations in different states or countries can be coupled into a givenbatch simulation farm. Such geographic distribution of servers leads todifficulties in communication and coordination that should be consideredwhen monitoring and utilizing instrumentation events within simulationmodels.

[0011] A batch simulation farm typically contains a number ofgeneral-purpose computers that perform as servers that create,distribute, and control the flow of simulation jobs throughout the batchsimulation farm. These servers perform simulation jobs, whose naturevaries with the specifics of the simulation methodology used andcomplexity of the digital device, which are then routed to simulationservers within the batch simulation farm for execution.

[0012] A simulation server executes the simulation job, taking note ofany failures and communicates pass/fail results back to servers withinthe batch simulation farm for logging and failed testcase storage foreventual debug. To allow for execution of tests on a large number ofdistributed systems, batch simulation farms will typically utilize aso-called “shared file system”. A shared file system allows a number ofdisparate general-purpose computers to share a common file system thatis located on shared disks in a central location. Examples of such filesystem are the Networked File System (NFS), the Andrew File System(AFS), and the Distributed File System (DFS). The shared files system isused to provide access to common control and data files used by thebatch simulation farm.

[0013] In addition to a shared file system, a number of well knownnetwork communication protocols are typically employed within a batchsimulation farm to enable distribution of files, communication andcoordination of servers, and inter-process communication among othertasks. Examples of these protocols are such things as File TransferProtocol or FTP, Sockets for direct network connections betweenprocesses on different computers, etc. These protocols are well know tothose skilled in the art and are not specific to batch simulation farms,but rather are common to all networking in modern general purposecomputers.

[0014] A batch simulation farm typically must run in a largelyautonomous fashion on a full time basis. This is to allow for thecontinuous execution of simulation tests without requiring continuoususer intervention and direction. This autonomous background execution oftests also makes it possible for so-called “cycle-stealing” on machinesnot specifically dedicated to simulation. That is to say,general-purpose computers that are normally used by users can executesimulation tests in a background mode. In this background mode, thesimulation task can take advantage of the otherwise idle computeresources on a large number of user machines. In addition, it is commonfor a large number of different simulation models to be active within abatch simulation farm at a given time.

[0015] The need for autonomous execution of large numbers of simulationjobs for a wide range of different models leads to certain challenges inmonitoring and controlling instrumentation events within these modelsthat must be overcome. Among the challenges that arise in the context ofa batch simulation farm, is that of faulty fail instrumentation eventswhich may result in a large number of testcases being erroneouslyreported as failing and subsequently being erroneously stored foranalysis.

[0016] A need exists for providing centralized disablement ofinstrumentation events without the need to recompile or redistributesimulation models. The present invention addresses such a need in amanner that is particularly useful for centrally disabling a faulty orundesired fail instrumentation event within a simulation model that maybe simulated within any number of simulation clients within the batchsimulation farm.

SUMMARY OF THE INVENTION

[0017] A method and system are disclosed herein for tracking frequentlyoccurring fail events that are detected during testcase simulation of asimulation model within a batch simulation farm in which testcases areexecuted within respect to a simulation model on one or more simulationclients. In accordance with the method of the present invention, theinstrumentation server receives fail event packets from one or moresimulation clients. The fail event packets contains an aggregate ofdetected occurrences of a specified fail event. The instrumentationserver monitors the rate of occurrence of the specified fail event fromreceived fail event packets to detect an excess rate of occurrence ofthe specified fail event.

[0018] All objects, features, and advantages of the present inventionwill become apparent in the following detailed written description.

BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The novel features believed characteristic of the invention areset forth in the appended claims. The invention itself however, as wellas a preferred mode of use, further objects and advantages thereof, willbest be understood by reference to the following detailed description ofan illustrative embodiment when read in conjunction with theaccompanying drawings, wherein:

[0020]FIG. 1 is a pictorial representation of a data processing system;

[0021]FIG. 2 depicts a representative hardware environment of the dataprocessing system illustrated in FIG. 1;

[0022]FIG. 3A is a simplified block diagram illustrating a digitaldesign entity that may be instrumented in accordance with the teachingsof the present invention;

[0023]FIG. 3B is a diagrammatic representation depicting a simulationmodel that may be instrumented in accordance with the teachings of thepresent invention;

[0024]FIG. 3C is a flow diagram illustrating of a model build processthat may be implemented in accordance with the teachings of the presentinvention;

[0025]FIG. 3D is a block diagram depicting data structures that may beinstrumented in accordance with the teachings of the present invention;

[0026]FIG. 4A is a simplified block diagram representative of aninstrumentation entity;

[0027]FIG. 4B is a simplified block diagram of a simulation modelinstrumented in accordance with the teachings of the present invention;

[0028]FIG. 4C illustrates exemplary sections of HDL syntax that maybeutilized in accordance with the teachings of the present invention;

[0029]FIG. 4D is a flow diagram depicting a model build process inaccordance with the teachings of the present invention;

[0030]FIG. 4E is a block diagram representation of memory datastructures constructed in accordance with the teachings of the presentinvention;

[0031]FIG. 5A is a logic diagram representation of a runtime disablemechanism in accordance with the teachings of the present invention;

[0032]FIG. 5B is a block diagram representation of functional unitsutilized to execute the method and system of the present invention on ahardware simulator in accordance with the teachings of the presentinvention;

[0033]FIG. 6A is a simplified gate level representation of an exemplarycounting instrument with a runtime disable feature and automaticclocking adjustment in accordance with the teachings of the presentinvention;

[0034]FIG. 6B is a simplified timing diagram illustrating automaticclocking adjustment of counting instrumentation;

[0035]FIG. 7 depicts an alternative counting means that may be employedfor counting events detected by instrumentation entities in accordancewith the teachings of the present invention;

[0036]FIG. 8A illustrates a conventional finite state machine that maybe instrumented with an embedded checker in accordance with theteachings of the present invention;

[0037]FIG. 8B depicts a conventional finite state machine design entity;

[0038]FIG. 8C illustrates a hardware description language file includingembedded instrumentation in accordance with the teachings of the presentinvention;

[0039]FIG. 9 depicts a hardware description language design entityincluded embedded instrumentation in accordance with the teachings ofthe present invention;

[0040]FIG. 10A is a block diagram illustrating a simulation modelcontaining a number of design and instrumentation entities;

[0041]FIG. 10B depicts a data structure for declaring an event within asimulation model in accordance with one embodiment of the presentinvention;

[0042]FIG. 10C illustrates a list of extended event data structures forthe simulation model in FIG. 10A;

[0043]FIG. 10D depicts a data structure for declaring an event within asimulation model in accordance with an alternate embodiment of thepresent invention;

[0044]FIG. 11A is a block diagram illustrating a simulation model inwhich the hierarchical event processing of the present invention isapplicable;

[0045]FIG. 11B depicts a set of input port mapping comments forperforming hierarchical processing of simulation model events inaccordance with a first embodiment of the present invention;

[0046]FIG. 11C illustrates a set of input port mapping comments forperforming hierarchical processing of simulation model events inaccordance with a second embodiment of the present invention;

[0047]FIG. 12A depicts a representative target design entity with aninstrumentation entity containing random instrumentation logicimplemented in accordance with the teachings of the present invention;

[0048]FIG. 12B illustrates an exemplary HDL file for implementinginstrumentation logic within an HDL design entity in accordance with theteachings of the present invention;

[0049]FIG. 13A depicts an exemplary design entity containing a multi-bitsimulation signal;

[0050]FIG. 13B illustrates a design entity wherein signal injection isimplemented in accordance with the teachings of the present invention;

[0051]FIG. 13C depicts an exemplary HDL source file that describesinstrumentation entity in accordance with the teachings of the presentinvention;

[0052]FIG. 13D illustrates an HDL design entity source code file whereina set of random instrumentation comments implement the logic necessaryfor selectively overriding a simulation signal in accordance with theteachings of the present invention;

[0053]FIG. 14A is a block diagram depicting data content within a mainmemory during a simulation run of a simulation model;

[0054]FIG. 14B is a block diagram illustrating data contents of a mainmemory during a simulation run in accordance with the teachings of thepresent invention;

[0055]FIG. 14C depicts an exemplary HDL source code file that describesan instrumentation entity in accordance with the teachings of thepresent invention;

[0056]FIG. 15 illustrates an eventlist file 1660 for the count events ofsimulation model 1000 shown in FIG. 10A;

[0057]FIG. 16B depicts a batch simulation farm in which a preferredembodiment of the present invention may be implemented;

[0058]FIG. 16C is a flow diagram illustrating a progression of eventsfrom the creation of a specific simulation model to the removal of thatmodel from batch simulation farm and instrumentation server inaccordance with a preferred embodiment of the present invention;

[0059]FIG. 16D is a flow diagram depicting steps performed duringexecution of a simulation job within a batch simulation farm inaccordance with a preferred embodiment of the present invention;

[0060]FIG. 17A is a block diagram illustrating the active data contentwithin a main memory during a simulation run of a simulation modelwithin a batch simulation farm environment in accordance with apreferred embodiment of the present invention;

[0061]FIG. 17B depicts an aggregate data packet delivered by an APIentry point routine to an instrumentation server in accordance with apreferred embodiment of the present invention;

[0062]FIG. 17C is a flow diagram illustrating a process by which thecorrectness of aggregate data packets received by an batch simulationfarm instrumentation server is validated in accordance with a preferredembodiment of the present invention;

[0063]FIG. 18A illustrates memory contents of a simulation client duringexecution of a simulation job in accordance with a preferred embodimentof the present invention;

[0064]FIG. 18B is a flow diagram depicting steps performed by an APIentry point in accessing a batch simulation farm instrumentation serverto obtain a disable failure list in accordance with a preferredembodiment of the present invention;

[0065]FIG. 19A is a block diagram illustrating memory contents of asimulation client at the conclusion of a simulation job in accordancewith a preferred embodiment of the present invention;

[0066]FIG. 19B is a flow diagram depicted a process by which a batchsimulation farm instrumentation server processes fail event aggregatedata packets in accordance with a preferred embodiment of the presentinvention;

[0067]FIG. 20A is a block diagram illustrating the active memory contentof a simulation client during simulation model testing in which countevent data delivered to an instrumentation server within a batchsimulation farm environment in accordance with a preferred embodiment ofthe present invention;

[0068]FIG. 20B depicts an aggregate count event packet delivered by anAPI entry point routine to an instrumentation server in accordance witha preferred embodiment of the present invention;

[0069]FIG. 20C illustrates a count storage file maintained within abatch simulation farm instrumentation server in accordance with apreferred embodiment of the present invention;

[0070]FIG. 20D depicts a counter directory/subdirectory structuremaintained within a batch simulation farm instrumentation server inaccordance with a preferred embodiment of the present invention;

[0071]FIG. 20E illustrates a count event entity translation tablederived from multiple entity list files in accordance with a preferredembodiment of the present invention;

[0072]FIG. 20F depicts a system applicable within a batch simulationfarm for storing and accessing count event data in accordance with apreferred embodiment of the present invention;

[0073]FIG. 20G illustrates a hierarchical and a non-hierarchical basiccounter output report in accordance with the teachings of the presentinvention;

[0074]FIG. 20H depicts a user-modifiable counter query data structureapplicable to the counter storage and access system shown in FIG. 20F;

[0075]FIG. 20I is a flow diagram illustrating steps performed by acounter query engine program to produce a basic counter output reportfrom a user query in accordance with a preferred embodiment of thepresent invention;

[0076]FIG. 21A depicts a system applicable within a batch simulationfarm for storing and accessing trends in count event data in accordancewith a preferred embodiment of the present invention;

[0077]FIG. 21B illustrates file and instruction means required for countdifference analysis in accordance with a preferred embodiment of thepresent invention;

[0078]FIG. 21C is a high-level flow diagram depicting steps performedwithin a batch simulation farm instrumentation server during countdifference analysis processing in accordance with a preferred embodimentof the present invention;

[0079]FIG. 21D is a flow diagram illustrating steps performed within abatch simulation farm instrumentation server during counter outputreport comparison processing in accordance with a preferred embodimentof the present invention;

[0080]FIG. 22A illustrates elements within a batch simulation farmutilized in collecting harvest event testcases in accordance with apreferred embodiment of the present invention;

[0081]FIG. 22B is a flow diagram depicting steps performed in collectingharvest event testcases in accordance with a preferred embodiment of thepresent invention;

[0082]FIG. 22C is a flow diagram illustrating the operation of a harvestmanager program during harvest event testcase collection in accordancewith a preferred embodiment of the present invention;

[0083]FIG. 23A depicts additional elements within an instrumentationserver and a harvest testcase server that are utilized in resolvinginconsistencies between a master harvest hit table and a harvesttestcase bucket in accordance with a preferred embodiment of the presentinvention;

[0084]FIG. 23B illustrates the data structure and content of a harvesttestcase list and a master harvest hit table as they exist prior to theharvest annealing process of the present invention; and

[0085]FIG. 23C is a flow diagram illustrating in further detail thesteps performed by a harvest manager program to process annealingrequests from a harvest testcase server in accordance with a preferredembodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0086] The present invention provides for accurate and comprehensivemonitoring of a digital circuit design in which a designer createsinstrumentation modules utilizing the same hardware description language(HDL) as utilized for the design itself. HDLs, while suited to the needsof digital designers can also be effectively utilized for a number ofchecking functions. In accordance with the Method and System of thepresent invention, instrumentation modules are utilized to monitorspecified design parameters while not becoming compiled as an integralpart of the design itself. Furthermore, since the instrumentationmodules are written in the same HDL as utilized in the actual design,such modules are platform and simulator independent. Unlike checkingdone with C or C++ programs, HDL instrumentation can be compiled and rundirectly without loss of performance on hardware simulators.

[0087] With reference now to the figures, and in particular withreference to FIG. 1, there is depicted a pictorial representation of adata processing system 10 with which the present invention may beadvantageously utilized. As illustrated, data processing system 10comprises a workstation 12 to which one or more nodes 13 are connected.Workstation 12 preferably comprises a high performance multiprocessorcomputer, such as the RISC System/6000 or AS/400 computer systemsavailable from International Business Machines Corporation (IBM).Workstation 12 preferably includes nonvolatile and volatile internalstorage for storing software applications comprising an ECAD system,which can be utilized to develop and verify a digital circuit design inaccordance with the method and system of the present invention. Asdepicted, nodes 13 are comprised of a display device 14, a keyboard 16,and a mouse 20. The ECAD software applications executed withinworkstation 12 preferably display a graphic user interface (GUI) withindisplay screen 22 of display device 14 with which a digital circuitdesigner can interact using a keyboard 16 and mouse 20. Thus, byentering appropriate inputs utilizing keyboard 16 and mouse 20, thedigital circuit designer is able to develop and verify a digital circuitdesign according to the method described further hereinbelow.

[0088]FIG. 2 depicts a representative hardware environment of dataprocessing system 10. Data processing system 10 is configured to includeall functional components of a computer and its associated hardware.Data processing system 10 includes a Central Processing Unit (“CPU”) 24,such as a conventional microprocessor, and a number of other unitsinterconnected via system bus 26. CPU 24 includes a portion of dataprocessing system 10 that controls the operation of the entire computersystem, including executing the arithmetical and logical functionscontained in a particular computer program. Although not depicted inFIG. 2, CPUs such as CPU 24 typically include a control unit thatorganizes data and program storage in a computer memory and transfersthe data and other information between the various parts of the computersystem. Such CPUs also generally include an arithmetic unit thatexecutes the arithmetical and logical operations, such as addition,comparison, multiplications and so forth. Such components and units ofdata processing system 10 can be implemented in a system unit such asworkstation 12 of FIG. 1.

[0089] Data processing system 10 further includes random-access memory(RAM) 28, read-only memory (ROM) 30, display adapter 32 for connectingsystem bus 26 to display device 14, and I/O adapter 34 for connectingperipheral devices (e.g., disk and tape drives 33) to system bus 26. RAM28 is a type of memory designed such that the location of data stored init is independent of the content. Also, any location in RAM 28 can beaccessed directly without having to work through from the beginning. ROM30 is a type of memory that retains information permanently and in whichthe stored information cannot be altered by a program or normaloperation of a computer.

[0090] Display device 14 provides the visual output of data processingsystem 10. Display device 14 can be a cathode-ray tube (CRT) based videodisplay well known in the art of computer hardware. However, with aportable or notebook-based computer, display device 14 can be replacedwith a liquid crystal display (LCD) based or gas plasma-based flat-paneldisplay. Data processing system 10 further includes user interfaceadapter 36 for connecting keyboard 16, mouse 20, speaker 38, microphone40, and/or other user interface devices, such as a touch-screen device(not shown), to system bus 26. Speaker 38 is one type of audio devicethat may be utilized in association with the method and system providedherein to assist diagnosticians or computer users in analyzing dataprocessing system 10 for system failures, errors, and discrepancies.Communications adapter 42 connects data processing system 10 to acomputer network. Although data processing system 10 is shown to containonly a single CPU and a single system bus, it should be understood thatthe present invention applies equally to computer systems that havemultiple CPUs and to computer systems that have multiple buses that eachperform different functions in different ways.

[0091] Data processing system 10 also includes an interface that resideswithin a machine-readable media to direct the operation of dataprocessing system 10. Any suitable machine-readable media may retain theinterface, such as RAM 28, ROM 30, a magnetic disk, magnetic tape, oroptical disk (the last three being located in disk and tape drives 33).Any suitable operating system and associated interface (e.g., MicrosoftWindows) may direct CPU 24. For example, the AIX operating system andAIX Windows windowing system can direct CPU 24. The AIX operating systemis IBM's implementation of the UNIX™ operating system. Othertechnologies also can be utilized in conjunction with CPU 24, such astouch-screen technology or human voice control.

[0092] Those skilled in the art will appreciate that the hardwaredepicted in FIG. 2 may vary for specific design and simulationapplications. For example, other peripheral devices such as optical diskmedia, audio adapters, or chip programming devices, such as PAL or EPROMprogramming devices well-known in the art of computer hardware and thelike, may be utilized in addition to or in place of the hardware alreadydepicted. In addition, main memory 44 is connected to system bus 26, andincludes a control program 46. Control program 46 resides within mainmemory 44, and contains instructions that, when executed on CPU 24,carries out the operations depicted in FIG. 4D and FIG. 4E describedherein.

[0093] Simulated digital circuit design models are comprised of at leastone and usually many sub-units referred to hereinafter as designentities. FIG. 3A is a block diagram representation of an exemplarydesign entity 300 in which the method and system of the presentinvention may be implemented. Design entity 300 is defined by a numberof components: an entity name, entity ports, and a representation of thefunction performed by design entity 300. Each entity within a givenmodel has a unique name (not explicitly shown in FIG. 3A) that isdeclared in the HDL description of each entity. Furthermore, each entitytypically contains a number of signal interconnections, known as ports,to signals outside the entity. These outside signals may be primaryinput/outputs (I/Os) of an overall design or signals connecting to otherentities within an overall design.

[0094] Typically, ports are categorized as belonging to one of threedistinct types: input ports, output ports, and bi-directional ports.Design entity 300 is depicted in as having a number of input ports 303that convey signals into design entity 300. Input ports 303 areconnected to input signals 301. In addition, design entity 300 includesa number of output ports 306 that convey signals out of design entity300. Output ports 306 are connected to a set of output signals 304.Bi-directional ports 305 are utilized to convey signals into and out ofdesign entity 300. Bi-directional ports 305 are in turn connected to aset of bi-directional signals 309. An entity, such as design entity 300,need not contain ports of all three types, and in the degenerate case,contains no ports at all. To accomplish the connection of entity portsto external signals, a mapping technique, known as a “port map”, isutilized. A port map (not explicitly depicted in FIG. 3A) consists of aspecified correspondence between entity port names and external signalsto which the entity is connected. When building a simulation model, ECADsoftware is utilized to connect external signals to appropriate ports ofthe entity according to a port map specification.

[0095] Finally, design entity 300 contains a body section 308 thatdescribes one or more functions performed by design entity 300. In thecase of a digital design, body section 308 contains an interconnectionof logic gates, storage elements, etc., in addition to instantiations ofother entities. By instantiating an entity within another entity, ahierarchical description of an overall design is achieved. For example,a microprocessor may contain multiple instances of an identicalfunctional unit. As such, the microprocessor itself will often bemodeled as a single entity. Within the microprocessor entity, multipleinstantiations of any duplicated functional entities will be present.

[0096] Referring now to FIG. 3B, there is illustrated a diagrammaticrepresentation of an exemplary simulation model 329 that may be utilizedin a preferred embodiment of the present invention. Simulation model 329consists of multiple hierarchical entities. For visual simplicity andclarity, the ports and signals interconnecting the entities withinsimulation model 329 have not been explicitly shown. In any model, oneand only one entity is the so-called “top-level entity”. A top-levelentity 320, is that entity which encompasses all other entities withinsimulation model 329. That is to say, top-level entity 320 instantiates,either directly or indirectly, all descendant entities within a design.Simulation model 329 consists of top-level entity 320 which directlyinstantiates two instances, 321 a and 321 b, of an FXU entity 321 and asingle instance of an FPU entity 322. Each instantiation has anassociated description, which contains an entity name and a uniqueinstantiation name. For top-level entity 320, description 310 is labeled“TOP:TOP”. Description 310 includes an entity name 312, labeled as the“TOP” preceding the colon, and also includes an instantiation name 314,labeled as the “TOP” following the colon.

[0097] It is possible for a particular entity to be instantiatedmultiple times as is depicted with instantiations 321 a and 321 b of FXUentity 321. Instantiations 321 a and 321 b are distinct instantiationsof FXU entity 321 with instantiation names FXU0 and FXU1 respectively.Top-level entity 320 is at the highest level within the hierarchy ofsimulation model 329. An entity that instantiates a descendant entitywill be referred to hereinafter as an “ancestor” of the descendantentity. Top-level entity 320 is therefore the ancestor that directlyinstantiates FXU entity instantiations 321 a and 321 b. At any givenlevel of a simulation model hierarchy, the instantiation names of allinstantiations must be unique.

[0098] In addition to FXU entity instantiations 321 a and 321 b,top-level entity 320 directly instantiates a single instance of a FPUentity 322 having an entity name FPU and instantiation name FPU0. Withinan entity description, it is common for the entity name to match theinstantiation name when only one instance of that particular entity isplaced at a given level of a simulation model hierarchy. However, thisis not required as shown by entity 322 (instantiation name FPU0, entityname FPU).

[0099] Within instantiation 321 a of FXU entity 321, single instanceentities 325 a and 326 a of entity A 325 and entity B 326 respectively,are directly instantiated. Similarly instantiation 321 b of the same FXUentity contains instantiations 325 b and 326 b of entity A 325 andentity B 326 respectively. In a similar manner, instantiation 326 a andinstantiation 326 b each directly instantiate a single instance ofentity C 327 as entities 327 a and 327 b respectively. The nesting ofentities within other entities can continue to an arbitrary level ofcomplexity provided that all entities instantiated, whether singly ormultiply, have unique entity names and the instantiation names at anygiven level of the hierarchy are unique with respect to one another.Each entity is constructed from one or more HDL files that contain theinformation necessary to describe the entity.

[0100] Associated with each entity instantiation is a so called“instantiation identifier”. The instantiation identifier for a giveninstantiation is a string consisting of the enclosing entityinstantiation names proceeding from the top-level entity instantiationname. For example, the instantiation identifier of instantiation 327 aof entity C 327 within instantiation 321 a of FXU entity 321 is“TOP.FXU0.B.C”. This identifier serves to uniquely identify eachinstantiation within a simulation model.

[0101] Referring now to FIG. 3C, there is depicted a flow diagram of amodel build process which may be implemented in a preferred embodimentof the present invention. The process begins with one or more designentity HDL source code files 340 and, potentially, one or more designentity intermediate format files 345, hereinafter referred to as “protofiles” 345, available from a previous run of an HDL compiler 342. HDLcompiler 342 processes HDL file(s) 340 beginning with the top levelentity of a simulation model and proceeding in a recursive fashionthrough all HDL or proto file(s) describing a complete simulation model.For each of HDL files 340 during the compilation process, HDL compiler342, examines proto files 345 to determine if a previously compiledproto file is available and consistent. If such a file is available andconsistent, HDL compiler 342 will not recompile that particular file,but will rather refer to an extant proto file. If no such proto file isavailable or the proto file is not consistent, HDL compiler 342explicitly recompiles the HDL file 340 in question and creates a protofile 344, for use in subsequent compilations. Such a process will bereferred to hereinafter as “incremental compilation” and can greatlyspeed the process of creating a simulation executable model 348.Incremental compilation is described in further detail hereinbelow. Oncecreated by HDL compiler 342, Proto files 344 are available to serve asproto files 345 in subsequent compilations.

[0102] In addition to proto files 344, HDL compiler 342 also creates twosets of data structures, design entity proto data structures 341 anddesign entity instance data structures 343, in memory 44 of computersystem 10. Design entity proto data structures 341 and design entityinstance data structures 343, serve as a memory image of the contents ofa simulation executable model 348. Data structures 341 and 343 arepassed, via memory 44, to a model build tool 346 that processes datastructures 341 and 343 into simulation executable model 348.

[0103] It will be assumed hereinafter that each entity is described by asingle HDL file. Depending on convention or the particular HDL in whichthe current invention is practiced, this restriction may be required.However, in certain circumstances or for certain HDLs it is possible todescribe an entity by utilizing more than one HDL file. Those skilled inthe art will appreciate and understand the extensions necessary topractice the present invention if entities are permitted to be describedby multiple HDL files. Furthermore, it will be assumed that there is adirect correspondence, for each entity, between the entity name and bothof the following: the name of the HDL file representing the entity, andthe name of the proto file for the entity.

[0104] In the following description, an HDL source code filecorresponding to a given entity will be referred to by an entity namefollowed by “.vhdl”. For example, the HDL source code file thatdescribes top-level entity 320 will be referred to as TOP.vhdl. Thislabeling convention serves as a notational convenience only and shouldnot be construed as limiting the applicability of the present inventionto HDLs other than VHDL.

[0105] Returning to FIG. 3B, it can be seen that each entity mayinstantiate, either directly or indirectly, one or more other entities.For example, the FXU entity directly instantiates A entity 325 and Bentity 326. Furthermore, B entity 326 directly instantiates C entity327. Therefore, FXU entity 321 instantiates, directly or indirectly, Aentity 325, B entity 326 and C entity 327. Those entities, that aredirectly or indirectly instantiated by another entity, will be referredto hereinafter as “descendants”. The descendants of top level entity 320are FXU entity 321, FPU entity 322, A entity 325, B entity 326, and Centity 327. It can be seen that each entity has a unique set ofdescendants and that each time an entity is instantiated, a uniqueinstance of the entity and its descendants is created. Within simulationmodel 329, FXU entity 321 is instantiated twice, FXU:FXU0 321 a andFXU:FXU1 321b, by top-level entity 320. Each instantiation of FXU entity321 creates a unique set of instances of the FXU, A, B, and C entities.

[0106] For each entity, it is possible to define what is referred to asa “bill-of-materials” or BOM. A BOM is a list of HDL files having dateand time stamps of the entity itself and the entity's descendants.Referring again to FIG. 3C, the BOM for an entity is stored in protofile 344 after compilation of the entity. Therefore, when HDL compiler342 compiles a particular HDL source code file among HDL files 340, aproto file 344 is generated that includes a BOM listing the HDL files340 that constitute the entity and the entity's descendants, if any. TheBOM also contains the date and time stamp for each of the HDL filesreferenced as each appeared on disk/tape 33 of computer system 10 whenthe HDL file was being compiled.

[0107] If any of the HDL files constituting an entity or the entity'sdescendants is subsequently changed, proto file 344 will be flagged asinconsistent and HDL compiler 342 will recompile HDL file 340 on asubsequent re-compilation as will be described in further detail below.For example, going back to FIG. 3B, the HDL files referenced by the BOMof FXU entity 321 are FXU.vhdl, A.vhdl, B.vhdl and C.vhdl, each withappropriate date and time stamps. The files referenced by the BOM oftop-level entity 320 are TOP.vhdl, FXU.vhdl, A.vhdl, B.vhdl, C.vhdl, andFPU.vhdl with appropriate date and time stamps.

[0108] Returning to FIG. 3C, HDL compiler 342 creates an image of thestructure of a simulation model in main memory 44 of computer system 10.This memory image is comprised of the following components: “proto” datastructures 341 and “instance” data structures 343. A proto is a datastructure that, for each entity in the model, contains information aboutthe ports of the entity, the body contents of the entity, and a list ofreferences to other entities directly instantiated by the entity (inwhat follows, the term “proto” will be utilized to refer to thein-memory data structure described above and the term “proto file” willbe utilized to describe intermediate format file(s) 344). Proto files344 are therefore on-disk representations of the in-memory proto datastructure produced by HDL compiler 342.

[0109] An instance data structure is a data structure that, for eachinstance of an entity within a model, contains the instance name for theinstance, the name of the entity the instance refers to, and the portmap information necessary to interconnect the entity with externalsignals. During compilation, each entity will have only one proto datastructure, while, in the case of multiple instantiations of an entity,each entity may have one or more instance data structures.

[0110] In order to incrementally compile a model efficiently, HDLcompiler 342 follows a recursive method of compilation in whichsuccessive entities of the model are considered and loaded from protofiles 345 if such files are available and are consistent with the HDLsource files constituting those entities and their descendants. For eachentity that cannot be loaded from existing proto files 345, HDL compiler342 recursively examines the descendants of the entity, loads thosedescendant entities available from proto file(s) 345 and creates, asneeded, proto files 344 for those descendants that are inconsistent withproto files 345. Psuedocode for the main control loop of HDL compiler342 is shown below (the line numbers to the right of the psuedocode arenot a part of the psuedocode, but merely serve as a notationalconvenience). process_HDL_file(file)  5 { 10 if (NOT proto_loaded(file)){ 15 if (exists_proto_file(file) AND check_bom(file)) { 20load_proto(file); 25 } else { 30 parse_HDL_file(file) 35 for (allinstances in file) { 40 process_HDL_file(instance); 45 } 50create_proto(file); 55 write_proto_file(file); 60 } 65 } 70create_instance(file): 75 } 80

[0111] When compiler 342 is initially invoked, no proto data structures341 or instance data structures 343 are present in memory 44 of computersystem 10. The main control loop, routine process_HDL_file( ) (line 5),is invoked and passed the name of the top level entity by means ofparameter “file”. The algorithm first determines if a proto datastructure for the current entity is present in memory 44 by means ofroutine proto_loaded( ) (line 15). Note that the proto data structurefor the top level entity will never be present in memory because theprocess starts without any proto data structures loaded into memory 44.If a matching proto data structure is present in memory 44, instancedata structures for the current entity and the current entity'sdescendants, if any, are created as necessary in memory 44 by routinecreate_instance( )(line 75).

[0112] However, if a matching proto data structure is not present inmemory 44, control passes to line 20 where routine exists_proto_file( )examines proto files 345 to determine if a proto file exists for theentity. If and only if a matching proto file exists, routine check_bom() is called to determine whether proto file 345 is consistent. In orderto determine whether the proto file is consistent, the BOM for the protofile is examined. Routine check_bom( ) examines each HDL source codefile listed in the BOM to determine if the date or time stamps for theHDL source code file have changed or if the HDL source code file hasbeen deleted. If either condition occurs for any file in the BOM, theproto file is inconsistent and routine check_bom( ) fails. However, ifcheck_bom( ) is successful, control is passed to line 25 where routineload_proto( ) loads the proto file and any descendant proto files intomemory 44, thus creating proto data structures 341 for the currententity and the current entity's descendants, if any. The construction ofprocess_HDL_file( ) ensures that once a proto file has been verified asconsistent, all of its descendant proto files, if any, are alsoconsistent.

[0113] If the proto file is either non-existent or is not consistent,control passes to line 35 where routine parse_HDL_file( ) loads the HDLsource code file for the current entity. Routine parse_HDL_file( ) (line35) examines the HDL source code file for syntactic correctness anddetermines which descendant entities, if any, are instantiated by thecurrent entity. Lines 40, 45, and 50 constitute a loop in which theroutine process_HDL_file( ) is recursively called to process thedescendent entities that are called by the current entity. This processrepeats recursively traversing all the descendants of the current entityin a depth-first fashion creating proto data structures 341 and protodata files 344 of all descendants of the current entity. Once thedescendant entities are processed, control passes to line 55 where a newproto data structure is created for the current entity in memory 44 byroutine create_proto( ). Control then passes to line 60 where a newproto file 344, including an associated BOM, is written to disk 33 byroutine write_proto_file( ). Finally, control passes to line 75 whereroutine create_instance( ) creates instance data structures 343 for thecurrent entity and any descendant entities as necessary. In this manner,process_HDL_file( ) (line 5) recursively processes the entire simulationmodel creating an in-memory image of the model consisting of proto datastructures 341 and instance data structures 343.

[0114] With reference now to FIG. 3D there is depicted a block diagramrepresenting compiled data structures which may be implemented in apreferred embodiment of the present invention. Memory 44 contains protodata structures 361, one for each of the entities referred to insimulation model 329. In addition, instantiations in simulation model329 are represented by instance data structures 362. Instance datastructures 362 are connected by means of pointers indicating thehierarchical nature of the instantiations of the entities withinsimulation model 329. Model build tool 346 in FIG. 3C processes thecontents of memory 44 into memory data structures in order to producesimulation executable model 348.

[0115] In order to instrument simulation models, the present inventionmakes use of entities known as “instrumentation entities,” which are incontrast to the entities constituting a design which are referred toherein as “design entities”. As with design entities, instrumentationentities are described by one or more HDL source code files and consistof a number of signal ports, a body section, and an entity name. In whatfollows, it will be assumed that an instrumentation entity is describedby a single HDL file. Those skilled in the art will appreciate andunderstand extensions necessary to practice the current invention for aninstrumentation entity that is described by multiple HDL files. Eachinstrumentation entity is associated with a specific design entityreferred to hereinafter as the “target entity”.

[0116] With reference now to FIG. 4A, there is illustrated a blockdiagram representation of an instrumentation entity 409. Instrumentationentity 409 includes a number of input ports 400 that are connected tosignals 401 within a target entity (not depicted in FIG. 4A). A bodysection 402 contains logic necessary to detect occurrences of specifiedconditions within the target entity and generate simulation model“events” with respect to signals 401. Three distinct types of events maybe generated: “count” events, “fail” events, and “harvest” events, eachdescribed below in turn. Body section 402 contains internal logic fordetecting occurrences of conditions precipitating generation of theseevents. A set of multi-bit output ports 403, 404, and 405 are connectedto external instrumentation logic (depicted in FIG. 4B) by means ofexternal signals 406, 407, and 408. Output ports 403, 404, and 405 thusprovide the connection from the internal logic in body section 402 tothe external instrumentation logic which is utilized to indicate theoccurrence of count, failure and harvest events.

[0117] A failure event is a sequence of signal values that indicate afailure in the correct operation of the simulation model. Eachinstrumentation entity monitors the target entity for any desired numberof failure events. Each occurrence of a failure event is assigned to aparticular signal bit on output port 403. Logic within body section 402produces an active high pulse on a specified bit of signal 403 when afailure condition is detected. Such activation of signal 403 is definedas a failure event. This error indication is conveyed by means ofexternal signal 406 to external instrumentation logic (depicted in FIG.4B as external instrumentation logic block 420), which flags theoccurrence of the failure event.

[0118] A count event is a sequence of signal values that indicate theoccurrence of an event within a simulation model for which it would beadvantageous to maintain a count. Count events are utilized to monitorthe frequency of occurrence of specific sequences within a simulationmodel. Each instrumentation entity can monitor the target entity for anydesired number of count events. Each count event is assigned to aparticular signal bit on output port 405. Logic block 402 contains thelogic necessary to detect the occurrence of the desired count events andproduces an active high pulse on the specified bit of signal 405 when acount event is detected. This count indication is conveyed by means ofexternal signal 408 to instrumentation logic, which contains countersutilized to record the number of occurrences of each count event.

[0119] The third event type, a harvest event, is a sequence of signalvalues that indicate the occurrence of a specific operativecircumstance, which would be advantageous to be able to reproduce. Whena harvest event occurs, a register within an external instrumentationlogic block is loaded to indicate at what point within a simulation runthe event occurred, and a flag is set to indicate the occurrence of thespecific circumstance. The details of the simulation run can thus besaved in order to recreate the specific circumstance monitored by theharvest event. Logic block 402 contains the logic necessary to detectthe harvest events.

[0120] Each instrumentation entity can detect any desired number ofharvest events that are each assigned to a particular signal bit onoutput port 404. Logic within block 402 produces an active high pulse onthe specified bit of signal 404 when a harvest event is detected. Thisharvest event detection is conveyed by means of external signal 407 toexternal instrumentation logic that contains a register and flag foreach harvest event.

[0121] The register is utilized to record at which point in thesimulation run the harvest event occurred, and the flag is utilized toindicate the occurrence.

[0122] With reference now to FIG. 4B, wherein is depicted a blockdiagram representation of simulation model 329 instrumented inaccordance with the teachings of the present invention. As can be seenin FIG. 4B, an instance 410 and an instance 411 of an instrumentationentity FXUCHK are utilized to monitor instances 321 a and 321 b of anFXU entity. For each FXU instantiations of 321 a and 321 b, an FXUCHKinstantiation, 410 and 411 respectively, is automatically generated bythe mechanism of the present invention. In a similar fashion,instrumentation entity FPUCHK 412 is instantiated to monitor FPU entity322.

[0123] As depicted in FIG. 4B, entity FXUCHK monitors a signals Q 372, asignal R 376, and a signal S 374 within each of instances 321 a and 321b of the FXU entity. Signal Q 372, is a signal within the instances 325a and 325 b of descendant entity A. Likewise, signal S 374 is a signalwithin descendant entity C that resides within descendant entity B.Finally, signal R 376 occurs directly within FXU entity 321. Although aninstrumentation entity may monitor any signal within a target entity orthe target entity's descendent entities, signals outside the targetentity cannot be monitored.

[0124] Each instrumentation entity is connected by means of fail, count,and harvest signals to instrumentation logic block 420 containing logicfor recording occurrences of each of the three event types. For thecount events monitored in simulation model 329, a set of counters 421 isutilized to count the number of occurrences of each count event. In asimilar manner, a set of flags 424 is utilized to record the occurrenceof failure events. Finally, a set of counters 422 and flags 423 arecombined and utilized to record the point at which a harvest eventoccurs and its occurrence, respectively. In one embodiment of thepresent invention, a cycle number is captured and stored utilizingcounters 422 and flags 423 to record a harvest event.

[0125] To facilitate instantiation and connection of instrumentationentities, instrumentation entity HDL source code files include aspecialized comment section, hereinafter referred to as “instrumentationentity description”, that indicates the target entity, the signalswithin the target entity to be monitored, and information specifyingtypes of events to be monitored.

[0126] With reference now to FIG. 4C, there is illustrated an exemplaryHDL file 440 that describes instrumentation entity FXUCHK depicted inFIG. 4B. HDL file 440 utilizes the syntax of the VHDL hardwaredescription language. In the VHDL language, lines beginning with twodashes, “——”, are recognized by a compiler as being comments. The methodand system of the present invention utilize comments of anon-conventional form to indicate information about an instrumentationentity. FIG. 4C depicts one embodiment of the present invention in whichcomments begin with two exclamation points in order to distinguish thesecomments from conventional comments in instrumentation HDL file 440. Itwill be appreciated by those skilled in the art that the exemplarysyntax utilized in FIG. 4C for the provision of unconventional commentsis but one of many possible formats.

[0127] Within HDL file 440, the I/O ports of a FXUCHK entity aredeclared in entity declaration 450. Within entity declaration 450, threeinput ports, S_IN, Q_IN, and R_IN, respectively, are declared. Inputports, S_IN, Q_IN, and R_IN, will be attached to signal S, 374, signalQ, 372, and signal R, 376 respectively as described below. Input port,CLOCK, is also declared and will be connected to a signal, CLOCK, withinthe FXU entity. In addition, three output ports: fails (0 to 1), counts(0 to 2), and harvests (0 to 1), are declared. These output portsprovide failure, count, and harvest signals for two failure events,three count events, and two harvest events. The names of the outputports are fixed by convention in order to provide an efficient means forautomatically connecting these signals to instrumentation logic block420.

[0128] A set of instrumentation entity descriptors 451 are utilized toprovide information about the instrumentation entity. As illustrated inFIG. 4C, descriptor comments 451 may be categorized in a number ofdistinct sections: prologue and entity name declaration 452, an inputport map 453, a set of failure message declarations 454, a set ofcounter declarations 455, a set of harvest declarations 456, and anepilogue 457.

[0129] The prologue and entity name 452 serve to indicate the name ofthe particular target entity that the instrumentation entity willmonitor. Prologue and entity name declaration 452 also serves as anindication that the instrumentation entity description has begun.Specifically, the comment “——!! Begin” within prologue and entity name452, indicates that the description of an instrumentation entity hasbegun. The comment “——!! Design Entity: FXU” identifies the targetentity which, in HDL file 440, is design entity FXU. This declarationserves to bind the instrumentation entity to the target entity.

[0130] Input port map 453 serves as a connection between the input portsof an instrumentation entity and the signals to be monitored within thetarget entity. The comments begin with comment “——!! Inputs” and endwith comment “——!! End Inputs”. Between these comments, comments of theform “——!! inst_ent_port_name=>trgt_ent_signal_name” are utilized, onefor each input port of the instrumentation entity, to indicateconnections between the instrumentation entity ports and the targetentity signals. The inst_ent_port_name is the name of theinstrumentation entity port to be connected to the target entity signal.The trgt_ent_signal_name is the name of the signal within the targetentity that will be connected to the instrumentation entity port.

[0131] In some cases a signal to be monitored lies within a descendantof a target entity. This is the case for signal S 374, which is embeddedwithin entity C which is a descendant of entity B 326 and target FXUentity 321. Input port map 453 includes an identification string forsignal S 374 which consists of the instance names of the entities withinthe target entity each separated by periods (“.”). This identificationstring is pre-pended to the signal name. The signal mapping commentwithin input port map 453 for signal S 374 is therefore as follows:

[0132] ——!! S_IN=>B.C.S

[0133] This syntax allows an instrumentation entity to connect to anysignal within the target entity or the target entity's descendantentities. A signal appearing on the top level of the target designentity, has no pre-pended entity names; and therefore, has the followingsignal mapping comment:

[0134] ——!!R_IN=>R

[0135] For signals on the top level of the target entity, a specialconnection method is provided. If the signal to be connected to has thesame name as its corresponding signal in the port map of theinstrumentation entity, no input port mapping comment is required andthe signal will be automatically connected if no such comment ispresent. In other words, if the input port mapping comment is of theform:

[0136] ——!! signal=>signal

[0137] where signal is a legal signal name without periods (“.”), thenthe input port mapping comment is not required and the system of thepresent invention will automatically make the connect. It is alsopossible to provide comments of the form given above to explicitlydenote the signal connection. This mechanism is only operative forsignals on the top level of the target entity.

[0138] Failure message declarations 454 begin with a comment of the form“——!! Fail Outputs;”, and end with a comment of the form “——!! End FailOutputs;”. Each failure event output is associated with a unique eventname and a failure message. This message may be output by the simulationrun-time environment upon detecting a failure event. The unique failureevent name is utilized to identify the specific failure event within themodel. Each failure event signal may be declared by a comment of theform “——!! n: <eventname>“failure message”;” where n is an integerdenoting the failure event to which the message is associated,<eventname>is the unique failure event name, and “failure message” isthe message associated with the particular failure event. One, and onlyone failure message declaration comment must be provided for eachfailure event monitored by the instrumentation entity.

[0139] Counter declaration comments 455 begin with a comment of the form“——!! Count Outputs;”, and end with a comment of the form “——!! EndCount Outputs;”. Each count event output is associated with a uniquevariable name. This name is associated with a counter in counter logic421 FIG. 4B. The variable name provides a means to identify andreference the particular counter associated with a particular countevent. Thus, a comment of the form “——!! n: <varname>qualifying_signal[+/−];” is associated with each counter event output. Within thisconvention, n is an integer denoting which counter event in theinstrumentation module is to be associated with a variable name“varname,” and qualifying_signal is the name of a signal within a targetdesign entity utilized to determine when to sample the count event pulseas will be further described hereinbelow. The parameter“qualifying_signal” is followed by “+/−” to specify whether thequalifying signal will be a high active qualifying signal or a lowactive qualifying signal.

[0140] Harvest declarations 456 begin with a prologue comment of theform “——!! Harvest Outputs;” and end with a comment of the form “——!!End Harvest Outputs;”. Each harvest event output is associated with aunique event name and a message that may be output by the simulationruntime environment when a harvest event has occurred during asimulation run. Each harvest event signal is declared in the form “——!!n: <eventname>“harvest message”;” where n is an integer denoting whichharvest event the message is to be associated with, <eventname>is theunique harvest event name and “harvest message” is the message to beassociated with the particular harvest event. One, and only one, harvestmessage declaration comment must be provided for each harvest eventmonitored by the instrumentation entity.

[0141] Harvest messages and event names, fail messages and event names,and counter variable names for a simulation model are included in asimulation executable model and lists of all the events within the modelare produced in separate files at model build time. In this manner, eachsimulation model includes the information for each event monitored and aseparate file containing this information for each event is available.Furthermore, as will be described below, the model build process nameseach event within the model (count, fail and harvest) model in such amanner as to insure that each event has a unique name with certainuseful properties.

[0142] Finally, epilogue comment 457 consists of a single comment of theform “——!! End;”, indicating the end of descriptor comments 451. Theremainder of instrumentation entity HDL file 440 that follows the I/Odeclarations described above, is an entity body section 458. In entitybody section 458, conventional HDL syntax is utilized to define internalinstrumentation logic necessary to detect the various events on theinput port signals and convey these events to the output port signals.

[0143] In addition to descriptor comments 451, that are located in theHDL source code file for an instrumentation entity, an additionalcomment line is required in the target entity HDL file. A comment of theform “——!! Instrumentation: name.vhdl”, where name.vhdl is the name ofthe instrumentation entity HDL file, is added to the target entity HDLsource code file. This comment provides a linkage between theinstrumentation entity and its target entity. It is possible to havemore than one such comment in a target entity when more than oneinstrumentation entity is associated with the target entity. These HDLfile comments will hereinafter be referred to as “instrumentation entityinstantiations”.

[0144] With reference now to FIG. 4D, there is depicted a model buildprocess in accordance with the teachings of the present invention. Inthis model build process, instrumentation load tool 464 is utilized toalter the in-memory proto and instance data structures of a simulationmodel thereby adding instrumentation entities to the simulation model.Instrumentation load tool 464 utilizes descriptor comments 451 withininstrumentation HDL files 461 to create instance data structures for theinstrumentation entities within a simulation model.

[0145] The model build process of FIG. 4D begins with design entity HDLfiles 340 and, potentially, one or more design entity proto files 345(available from a previous run of HDL compiler 462), instrumentationentity HDL files 460, and potentially, one or more instrumentationentity proto files 461 (available from a previous run of HDL compiler462). HDL compiler 462, processes design entity HDL files 340, andinstrumentation entity HDL files 460 following an augmentation ofalgorithm process_HDL_file( ) that provides for efficient incrementalcompilation of the design and instrumentation entities comprising asimulation model. HDL compiler 462 loads proto data structures fromdesign entity proto files 345 and instrumentation entity protos files460, if such proto files are available and consistent. If such protofiles are not available or are not consistent, HDL compiler 462 compilesdesign entity HDL files 340 and instrumentation entity HDL files 460 inorder to produce design entityproto files 344 and instrumentation entityproto files 468. (design entity proto files 344 and instrumentationentityproto files 468 are available to serve as design entity protofiles 345 and instrumentation entity proto files 460 respectively for asubsequent run of HDL compiler 462).

[0146] In addition, HDL compiler 462 creates in-memory design proto datastructures 463 and design instance data structures 465 for the designentities of a simulation model. HDL compiler 462 also creates in-memoryinstrumentation proto data structures 466 for the instrumentationentities of a simulation model.

[0147] In order to minimize processing overhead HDL compiler 462 neitherreads nor processes descriptor comments 451. However, HDL compiler 462does recognize instrumentation entity instantiation comments withintarget entity HDL files. As such, HDL compiler 462 cannot createinstance data structures instrumentation entity data structures 467. Thecreation of instance data structures requires interconnectioninformation contained within descriptor comments 451 not processed byHDL compiler 462. HDL compiler 462 does, however, create instrumentationproto data structures 466.

[0148] The in-memory design proto data structures 463, design instancedata structures 465, and instrumentation entity proto data structures466, are processed by instrumentation load tool 464. Instrumentationload tool 464 examines design entity proto data structures 463 anddesign entity instance data structures 465 to determine those designentities that are target entities. This examination is accomplished byutilizing a particular comment format as previously described.

[0149] All target entities that are loaded from design entity protofiles 345 contain an instantiation for any associated instrumentationentity. Therefore, instrumentation load tool 464 merely creates aninstance data structure 467 for any such instrumentation entity andpasses, the unaltered design proto data structure 463 to instrumenteddesign proto data structure 463 a, and passes design instance datastructure 465 to instrumented design instance data structure 465 a.

[0150] If however, a target entity is loaded from design entity HDLfiles 340, rather than from design entity proto files 345,instrumentation load tool 464 must alter its design proto data structure463 and its design instance data structure 465 to instantiate anassociated instrumentation entity. An instrumented design proto datastructure 463 a and instrumented design instance data structure 465 aare thereby produced. In addition, instrumentation load tool 464 createsan instrumentation instance data structure 467 for each instrumentationentity associated with the current design entity.

[0151] The design entity proto data structures 463 that are altered byinstrumentation load tool 464 are saved to disk 33 of computer system 10as design entity proto files 344. Design entity proto files 344, whichmay include references to instrumentation entities, are directly loadedby a subsequent compilation of a simulation model, thus savingprocessing by instrumentation load tool 464 on subsequent recompilationsunless an alteration is made to a design entity or an associatedinstrumentation entity.

[0152] In order for HDL compiler 462 to determine if alterations weremade to either a target design entity or the target design entity'sassociated instrumentation entities, the (BOM of a target design entityis expanded to include the HDL files constituting the instrumentationentities. In this manner, HDL compiler 462 can determine, by inspectionof the BOM for a given design entity, whether to recompile the designentity and the design entity's associated instrumentation entities orload these structures from proto files 345 and 461.

[0153] Finally, instrumentation load tool 464 creates a unique proto andinstance data structure for instrumentation logic block 420 and connectsthe fail, harvest, and count event signals from each instrumentationentity instantiation to instrumentation logic block 420. Model buildtool 446 processes in-memory proto and instance data structures 463 a,465 a, 467, 466 to produce instrumented simulation executable model 480

[0154] In HDL compiler 462, algorithm process_HDL_file( ) is augmentedto allow for the incremental compilation of design and instrumentationentities. A pseudocode implementation of a main control loop of HDLcompiler 462 is shown below: process_HDL_file2(file,design_flag) 5 { 10if (NOT proto_loaded(file)) { 15 if (exists_proto_file(file) ANDcheck_bom(file)) { 20 load_proto(file); 25 }else { 30parse_HDL_file(file) 35 for (all instances in file) { 40process_HDL_file2(instance, design_flag); 45 } 50 if (design_flag=TRUE){ 55 for (all instrumentation instances in file) { 60process_HDL_file2(instance, FALSE); 65 } 70 } 75 create_proto(file); 80write_proto_file(file); 90 } 95 } 100 if (design_flag = TRUE) { 105create_instance(file); 110 } 115 } 120

[0155] Algorithm process_HDL_file2( ) is an augmentation toprocess_HDL_file( ) of HDL compiler 342 in order to support the creationof instrumented simulation models. The algorithm is invoked with thename of the top level design entity passed through parameter file and aflag indicating whether the entity being processed is a design entity oran instrumentation entity passed through parameter design_flag(design_flag=TRUE for design entities and FALSE for instrumentationentities). Algorithm process_HDL_file2( ) (line 5) first checks, bymeans of routine proto_loaded( ) (line 15), if the proto for the currententity is already present in memory 44. If so, processing passes to line105. Otherwise, control is passed to line 20 and 25 where disk 33 ofcomputer system 10 is examined to determine if proto files for theentity and its descendants (including instrumentation entities, if any)exist and are consistent. If so, the appropriate proto files are loadedfrom disk 10 by routine load_proto( ) (line 25) creating proto datastructures, as necessary, in memory 44 for the current entity and thecurrent entity's descendants including instrumentation entities.

[0156] If the proto file is unavailable or inconsistent, control passesto line 35 where the current entity HDL file is parsed. For any entitiesinstantiated within the current entity, lines 40 to 55 recursively callprocess_HDL_file2( ) (line 5) in order to process these descendants ofthe current entity. Control then passes to line 55 where the design_flagparameter is examined to determine if the current entity being processedis a design entity or an instrumentation entity. If the current entityis an instrumentation entity, control passes to line 80. Otherwise, thecurrent entity is a design entity and lines 60 to 70 recursively callprocess_HDL_file2( ) (line 5) to process any instrumentation entitiesinstantiated by means of instrumentation instantiation comments. Itshould be noted that algorithm process_HDL_file2( ) (line 5) does notallow for instrumentation entities to monitor instrumentation entities.Any instrumentation entity instantiation comments within aninstrumentation entity are ignored. Control then passes to line 80 whereproto data structures are created in memory 44 as needed for the currententity and any instrumentation entities. Control then passes to line 90where the newly created proto data structures are written, as needed todisk 33 of computer system 10.

[0157] Control finally passes to line 105 and 110 where, if the currententity is a design entity, instance data structures are created asneeded for the current entity and the current entity's descendants. Ifthe current entity is an instrumentation entity, routinecreate_instance( ) (line 110) is not called. Instrumentation load tool464 is utilized to create the in-memory instance data structures forinstrumentation entities.

[0158] It will be apparent to those skilled in the art that HDL compiler462 provides for an efficient incremental compilation of design andinstrumentation entities. It should also be noted that the abovedescription is but one of many possible means for accomplishing anincremental compilation of instrumentation entities. In particular,although many other options also exist, much, if not all, of thefunctionality of instrumentation load tool 464 can be merged into HDLcompiler 462.

[0159] With reference now to FIG. 4E wherein is shown a depiction ofmemory 44 at the completion of compilation of simulation model 329 withinstrumentation entities FXUCHK and FPUCHK. Memory 44 contains protodata structures 481, one for each of the design and instrumentationentities referred to in simulation model 329. In addition, design andinstrumentation instances in simulation model 329 are represented byinstance data structures 482. The instance data structures are connectedby means of pointers indicating the hierarchical nature of theinstantiations of the design and instrumentation entities withinsimulation model 329.

[0160] With reference now to FIG. 5A, wherein is depicted failure flags424 of instrumentation logic block 420 in greater detail. Failure flags424 consist of registers 500 a-500 n utilized to accept and store anindication of the occurrence of a failure event. In what follows, theoperation of a single failure flag for a particular failure event 502will be discussed. The operation of all failure flags is similar.

[0161] Register 500 a holds a value that represents whether a failureevent 502 has occurred or not. Register 500 a is initially set to avalue of ‘0’ by the simulation run-time environment at the beginning ofa simulation run. When failure event 502, if enabled at register 507 a,occurs, register 500 a is set to a value of a logical ‘1’, therebyindicating the occurrence of a failure event. Register 500 a is drivenby logical OR gate 501. Logical OR gate 501 performs a logical OR of theoutput of register 500 a and a qualified failure signal 503 to createthe next cycle value for register 500 a. In this manner, once register500 a is set to a logical ‘1’ by the occurrence of an enabled failureevent, register 500 a maintains the value of a logical ‘1’ until resetby the simulation runtime environment. Likewise, register 500 amaintains a value of ‘0’ from the beginning of the simulation run untilthe occurrence of the failure event, if enabled.

[0162] Qualified failure signal 503 is driven by logical AND gate 505.Logical AND gate 505 produces, on qualified failure signal 503, thelogical AND of failure signal 506 and the logical NOT of register 507 a.Register 507 a serves as an enabling control for qualified failuresignal 503. If register 507 a contains a value of ‘0’, logical AND gate505 will pass failure event signal 506 unaltered to qualified failuresignal 503. In this manner, the monitoring of the failure event isenabled. Registers 507 a-507 n are set, by default, to a value of ‘0’.However, if register 507 a contains a value of a logical ‘1’, qualifiedfailure signal 503 will remain at a value of ‘0’ irrespective of thevalue of failure event signal 506, thereby disabling the monitoring offailure event 502. In this manner, register 508, consisting of registers507 a-507 n, can mask the occurrence of any subset of failure events inthe overall simulation model from registers 500 a-500 n.

[0163] To efficiently implement the ability to selectively disable themonitoring of failure events, the simulation run-time environmentincludes a function that allows a user to disable monitoring of aspecific failure event for a given instrumentation entity. This functionwill automatically set the appropriate registers among registers 507a-507 n within register 508 to disable the monitoring of a particularfailure event for every instance of the instrumentation entity withinthe overall simulation model. Instrumentation load tool 464 and modelbuild tool 446 encode sufficient information within instrumentedsimulation executable model 480 to determine which failure bits withinregister 508 correspond to which instrumentation entities.

[0164] The ability to selectively disable monitoring of failure eventsis of particular use in large batch-simulation environments. Typically,in such an environment, a large number of general purpose computers,running software or hardware simulators, are dedicated to automaticallyrunning a large number of simulation runs. If a simulation model with afaulty instrumentation entity that incorrectly indicates failure eventsis run in such an environment, a large number of erroneous failures willbe generated causing lost time. By selectively disabling failure eventswithin instrumentation entities, the present invention allows simulationto continue while only disabling erroneous failure signals rather thanhaving to disable all failure monitoring. This option is particularlyuseful when the process of correcting a faulty instrumentation entityand creating a new simulation model is substantially time consuming. Thepresent invention also provides similar enabling and disablingstructures for the harvest and count events within a model.

[0165] Logical OR gate 512 is utilized to produce a signal, 511, thatindicates whether any failure event within the model has occurred. Thissignal is utilized to allow hardware simulators to efficiently simulatesimulation models that have been instrumented according to the teachingsof the present invention.

[0166] With reference now to FIG. 5B there is illustrated in greaterdetail, features of the present invention utilized to support efficientexecution of an instrumented simulation model on a hardware simulator.It should be noted that for most hardware simulators, the operation ofpolling a facility within a simulation model during a simulation run isoften a time consuming operation. In fact, if facilities must be polledevery cycle, it is often the case that as much, if not considerablymore, time is spent polling a simulation model for results rather thanrunning the actual simulation. As such, it is advantageous when using ahardware simulator to avoid polling facilities within the model during asimulation run. In addition, many hardware simulators provide a facilitythat instructs the hardware simulator to run a simulation withoutinterruption until a specific signal within the simulation model attainsa specific value. This facility usually results in the highestperformance for a simulation run on a hardware simulator.

[0167] In order to execute simulation model 520 on a hardware simulator,a termination signal 513, is typically utilized as a means to avoidhaving to poll the model after each cycle. Typically, a hardwaresimulator will cycle simulation model 520 until signal 513 is assertedto a logical ‘1’. The assertion of termination signal 513 to a logical‘1’ indicates that a simulation run has finished. Without terminationsignal 513, it would be necessary to directly poll facilities withinsimulation model 520 to determine when a simulation run is completed.

[0168] To efficiently locate and diagnose problems in simulation model520, it is advantageous to allow a simulation run to be stoppedimmediately whenever a failure event occurs during simulation ofsimulation model 520 (harvest events and count events are typically onlypolled at the end of a simulation run). This allows a user to easilylocate the failure event within the simulation run, thereby facilitatingdebugging of the failure. In order to allow simulation models that havebeen instrumented according to the teachings of the present invention toefficiently execute on a hardware simulator, a comment of the form “——!!Model Done: signalname” is placed within the HDL source code file forthe top level entity of the simulation model where signalname is thename of termination signal 513 within the simulation model. This commentis only utilized if present in the HDL file for the top-level entity. Ifsuch a comment is present in the HDL source code file for the top levelentity, a logical OR gate 515 will automatically be included within thesimulation model. Logical OR gate 515 produces the logical OR of signals511 and 513 on signal 516. Signal 516 is therefore asserted to a logical‘1’ whenever the simulation run has completed (signal 513 high) or afailure event has occurred (signal 511 high). Consequently, by executingsimulation model 520 in a hardware simulator until signal 516 isasserted to a value of a logical ‘1’, the instrumentation for simulationmodel 520 can be combined and utilized along with existing simulationtermination techniques in a seamless manner. In the alternative, if thecomment indicating the name of termination signal 513 is not present,logical OR gate 515 is not included in the model and signal 511 isdirectly connected to signal 516. The name of signal 516 is fixed to aparticular name by convention.

[0169] In many simulators, the passage of time within the simulatedmodel is modeled on a cycle-to-cycle basis. That is to say, time isconsidered to pass in units known as cycles. A cycle is delineated bythe occurrence of a clock signal within a simulation model thatregulates the updating of storage elements within the design. Thesesimulators are commonly known as “cycle simulators”. A cycle simulatormodels a digital design by repeatedly propagating the values containedwithin storage elements through interconnecting logic that lies betweenstorage elements without specific regard for the physical timing of thispropagation, to produce next cycle values within the storage elements.In such simulators, a primitive storage element, hereinafter referred toas a “simulator latch”, is utilized to model the storage elements withina digital design. One simulator cycle therefore consists of propagatingthe current values of the simulator latches through the interconnectinglogic between storage elements and updating the simulator latches withthe next cycle value.

[0170] In many circumstances, however, it is not possible to utilize asingle simulator latch to directly model the storage elements within adesign. Many common storage elements utilized within digital designsoften require more than one simulator latch. For example, so calledmaster-slave flip-flops are generally modeled utilizing two simulatorlatches to accurately simulate the behavior of such storage elements. Inorder to efficiently model storage elements, a designer will typicallyrefer to a library that contains storage element simulation models foruse in a design. These design storage elements are modeled by one ormore simulator latches. Storage elements comprised of one or moresimulator latches that are implemented within a design will be referredto hereinbelow as “design latches”.

[0171] As a consequence of utilizing multiple simulator latches to modela design latch, the process of propagating the input of a design latchto its output, which constitutes a design cycle, often requires morethan one simulator cycle. A single design cycle is thus defined ascomprising the number of simulator cycles required to propagate a set ofvalues from one set of storage elements to the next.

[0172] In other circumstances, a simulation model may consist ofdistinct portions that are clocked at differing frequencies. Forexample, a microprocessor core connected to a bus interface unit, mayoperate at a higher frequency and than the bus interface unit. Underthese circumstances, the higher frequency portion of the design willrequire one or more simulator cycles, say N cycles, to simulate a singledesign cycle. The lower frequency portion of the design will require amultiple of N simulator cycles in order to simulate a design cycle forthe lower frequency portion. This multiple is equal to the ratio of thefrequency of the higher speed design portion to the frequency of thelower speed design portion. It is often the case that certain portionsof the logic can be run at a number of differing frequencies that areselectable at the beginning of a simulation run. Such logic, with arun-time selectable frequency of operation, presents unique challengesfor monitoring count events.

[0173] With reference now to FIG. 6A, there is depicted a gate levelrepresentation of exemplary logic for one counter of counters 421 withininstrumentation logic block 420 depicted in FIG. 4B. Each counter of 421is represented by a multi-bit simulator latch 600. Simulator latch 600is initialized by the simulation runtime environment to a value of zeroat the beginning of a simulation run. Simulator latch 600 is updatedevery simulator cycle and is driven by multiplexor 601. Multiplexor 601,controlled by selector signal 602, selects between signal 613, thecurrent value of simulator latch 600, and signal 605, the current valueof simulator latch 600 incremented by 1 by incrementor 604, to serve asthe next cycle value for simulator latch 600. By selecting signal 605,multiplexor 601 causes the counter value within simulator latch 600 tobe incremented when a count event occurs. It should be noted, however,that simulator latch 600 is updated every simulator cycle irrespectiveof the number of simulator cycles that correspond to a design cycle forthe logic being monitored by a counting instrument. Logical AND gate 606and simulator latch 607 serve to disable the monitoring of count eventsignal 609 in a manner similar to that described above for the disablingof failure events. Signal 608 is count event signal 609 furtherqualified by signal 610 by means of logical AND gate 611.

[0174] Signal 610 insures that simulator latch 600 will be incremented,if count event signal 609 is active, only once per design cycle for thelogic being monitored by a counting instrument irrespective of thenumber of simulation cycles utilized to model the design cycle. Thisclocking normalization is necessary to ensure that the event countsrecorded in counters 421 correspond directly to the number of designcycles the event occurred in and not the number of simulator cycles theevent occurred in. For example if an event occurs in two design cycleswhere design cycle require four simulators cycles, it is preferable tohave the event counter reflect a value of two rather than a value ofeight as would occur if the counter were allowed to update in everysimulator cycle.

[0175] Furthermore, if the count event being monitored is within aportion of the logic with a run-time selectable frequency of operation,it is useful to have the count registers reflect the number ofoccurrences of the event in terms of design cycles. For example,consider a circumstance where a count event occurs twice during twodifferent simulation runs. In the first run, assume that four simulatorcycles are needed to represent each design cycle. Further assume in thesecond run that twelve simulator cycles are necessary to represent eachdesign cycle. Without a clocking normalization mechanism, the first runwould indicate that the event occurred eight times (two occurrencestimes four simulator cycles per occurrence) and the second run wouldindicate that the event occurred twenty-four times (two occurrencestimes twelve simulator cycles per occurrence) when in fact the eventactually only occurred twice in both simulation runs. Therefore, itwould be advantageous to limit the updating of counters 421 such thateach counter is only updated once per design cycle irrespective of thenumber of simulator cycles, possibly variable at run-time, needed torepresent a design cycle.

[0176] In simulation models in which multiple simulator cycles areutilized to represent a single design cycle, explicit clocking signalsare utilized within the model to control the updating of the variousdesign storage elements. These clocking signals specify in whichsimulator cycles the simulator latches representing design storageelements are allowed to update. A clocking signal is asserted high forsome contiguous number of simulator cycles either at the beginning orend of the design cycle and asserted low for the remaining simulatorcycles within the design cycle. If the clocking signal is asserted highduring the beginning of the design cycle, the clock is referred to as a“high-active” clock and, likewise, if the clocking signal is assertedlow during the beginning of the design cycle, the clock is referred toas a “low-active” clock.

[0177] Each count event signal has an associated qualifying signal asspecified by counter declaration comments 455 as described above.Typically, these qualifying signals are connected to the clockingsignals within the design responsible for updating the storage elementswithin the portion of logic monitored by the count event. The qualifyingsignal for the count event for simulator latch 600, qualifying signal612, is depicted as a high-active qualifier signal. Qualifying signal612 is processed by simulator latch 613 and logical AND gate 614, toproduce signal 610 which is active high for one and only one simulatorcycle within the design cycle delineated by qualifying signal 612.

[0178] Turning now to FIG. 6B there is illustrated a simplified timingdiagram that demonstrates operation of simulator latch 613 and logicalAND gate 614 assuming clocking qualifying signal 612 is a high activeclocking signal of fifty percent duty cycle for a design cycle thatoccurs over a 10-simulation cycle period. Signal 615, the output ofsimulator latch 613, is qualifying signal 612 delayed by one simulatorcycle. Signal 615 is inverted and logically ANDed with qualifying signal612 to produce signal 610, a high-active pulse that is asserted for thefirst simulator cycle of the design cycle. In a similar fashion, if thequalifying clock signal is low active, qualifying signal 612 would beinverted and signal 615 would be uninverted by logical AND gate 614.This would produce a single simulator cycle active high pulse during thefirst simulator cycle of the design cycle. Qualifying signal 610, byqualifying count event signal 609 by means of logical AND gate 611,insures that counter 600 is incremented only once per design cycleirrespective of the number of simulator cycles utilized to represent adesign cycle.

[0179] In contrast to cycle simulators, another class of simulators knowas “event-driven” simulators is commonly utilized. In an event drivensimulator, time is modeled in a more continuous manner. Each rising orfalling edge of a signal or storage element within a design is modeledwith specific regard to the physical time at which the signal transitionoccurred. In such simulators, the simulator latches operate in aslightly different manner than for a cycle based simulator. A simulatorlatch in an event driven simulator is controlled directly by a clockingsignal. A new value is loaded into the simulator latch on either therising or falling edge of the clocking signal (called a “positive-edgetriggered” latch and a “negative-edge triggered” latch respectively). Topractice the current invention within an event driven simulator, latch613 and logical gates 614 and 611 are unnecessary. Rather, counter latch600 is replaced by a positive or negative edge triggered simulator latchbased on the polarity of qualifying signal 612. Qualifying signal 612 isconnected directly to simulator latch 600 and directly controls theupdates of counter latch 600 insuring that the latch is updated onlyonce per design cycle.

[0180] Returning to FIG. 6A, incrementor 604 represents but one possiblemechanism that may be utilized to implement the next logic state for agiven counter within the present invention. As depicted in FIG. 6A,incrementor 604 ensures that counters 421 within a model are cycledthrough a series of values whose binary patterns correspond to thecustomary representation of non-negative integers. In one embodiment ofthe present invention, incrementor 604 is comprised of an adder thatincrements the current value of counter 600 by a unit value each timesignal 605 is selected by selector signal 602. This exemplaryimplementation provides for convenience of decoding the value of counter600 at the termination of a simulation run, but does so at a cost inoverhead that is not acceptable in many simulators.

[0181] For software simulators, one of two basic approaches may beutilized to model an incremetor, such as incrementor 604. In the firstapproach, the incrementor is modeled directly by an ADD or INCREMENTinstruction in the simulation execution model. When incrementors aremodeled directly as a single instruction within the simulation executionmodel, the use of incrementor 604 provides for efficient counters withina simulation execution model.

[0182] However, many software simulators and virtually all hardwaresimulators model incrementor functions as a set of gates that arereplicated essentially without change at each bit position of thecounter. Within a software simulator, these gates must be translatedinto a sequence of instructions. In a hardware simulator, these gatesare explicitly replicated for each counter as individual gates. Due toimplementation or structural limitations, many software simulators areincapable of modeling an incrementor in any other manner than as a setof gates. Clearly, for these software simulators that must modelincrementors as a number of gates and therefore as a sequence ofinstructions, a performance loss will result over those softwaresimulators that model incrementors as a single increment or addinstruction. Likewise, for hardware simulators, the number of gatesrequired for each adder, which must be modeled directly by gates withinthe hardware simulator, can prove to be a significant burden.

[0183] The method and system of the present invention alleviate thesedifficulties by implementing a linear feedback shift register as thecounting means within counting instrumentation. As explained below,appropriate configuration and utilization of such a liner feedback shiftregister results in an efficient method of incrementing a counter thatavoids the overhead associated with incrementor 604.

[0184] With reference now to FIG. 7, there is depicted a linear feedbackshift register (LFSR) counter 700 consisting of a shift register 704 and“exclusive NOR” (XNOR) gate 706. Various methods of constructing LFSRsare known to those skilled in the art. As illustrated in FIG. 7, LFSRcounter 700 includes a modified shift register 704 that may replaceregister 600 and incrementor 604 of FIG. 6A. LFSR counter 700 alsoincludes multiplexor 601 (replicated bit-by-bit within LFSR 704) whichprovide feedback paths 616. Feedback path 616 provides a means for shiftregister 704 to maintain its current value during those simulator cyclesin which no count pulse trigger (signal 602) is received. For hardwareand software design simulators in which, for logistical or otherreasons, incrementation of counters must be accomplished utilizing a setof gates for each counter, shift register 704 replaces register 600within the counter logic depicted in FIG. 6A. The need for incrementor604 is thus eliminated and is replaced by XNOR gate 706. In this manner,register 600 and incrementor 604 are replaced utilizing a more efficientlogic structure having substantially reduced overhead. Counters 421 ofFIG. 4B, will therefore consist of LFSR-based configurations such asLFSR counter 700 whose values can be decoded at the end of a simulationrun to reveal their corresponding integral values.

[0185] Shift register 704 can be of any desired length. In a preferredembodiment, shift register 704 is a 22 bit register, although larger orsmaller registers may be employed. Shift register 704 consists oflatches 718 arranged in a serial fashion such that a given latch'soutput is utilized as input to the next latch 718 within shift register704. In addition, a select subset of latches 718 within shift register704 have their outputs sourced to XNOR gate 706. XNOR gate 706 isutilized to provide an input for the first latch within shift register704.

[0186] The LFSR is a logic structure that, when properly configured,will sequence through all possible bit patterns with the exception ofthe all-ones pattern (it is possible to construct LFSRs which excludethe all-zeros pattern or LFSRs that cycle through all possible bitpatterns). For example, in a 22 bit LFSR, bits 1 and 22 may be selectedfor inputs to XNOR gate 706 to provide a sequence of bit patterns inshift register 704 which traverses every possible permutation with theexception of the all-ones pattern. Shift register 704 must be loadedwith an initial value that is not the all ones pattern. This may beaccomplished automatically by initializing all latches to a binary zerovalue within the simulator, or by utilizing the control program thatdrives the simulator to explicitly set these latches to binary zeros.

[0187] After initialization, the numeric pattern held by bit positions718 of shift register 704 will cycle through a specific and predictablepattern in a repeating fashion. That is to say, for any given bitpattern present in shift register 704, there is a specific, uniquepattern the shift register will subsequently assume upon being shiftedand therefore, the sequence of patterns through which the shift registercycles is fixed and repeats in a predictable manner. Due to theseproperties, LFSR counter 700 can be utilized as a counting means withinfor the instrumentation detection means previously described. Byassigning the value of “zero” to a pre-selected starting value (say theall zeros pattern for shift register 704), the value of “one” to thenext bit pattern formed by shifting the LFSR, and so on, the LFSR canserve as a counter. To be useful as a counter, the bit patterns withinshift register 704 must be converted back to their corresponding integervalues. This is easily accomplished for LFSRs with a small number ofbits (less than 25 bits) by means of a lookup table consisting of anarray of values, where the index of the array corresponds to the LFSRbit pattern value and the entry in the array is the decoded integervalue for the LFSR. For LFSRs with a larger number of bits, softwaredecoding techniques can be utilized to decode the LFSR value bysimulating the operation of the LFSR.

[0188] As illustrated in FIG. 7, the logic necessary to implement LFSRcounter 700 consists of the single XNOR gate 706 with two feedbackinputs. While the number of required feedback gates and inputs theretomay vary in proportion to different possible lengths of an LFSR, ingeneral, for typical LFSRs (less than 200 bits), only one XNOR gate witha relatively small number of inputs (less than 5 bits) is required. Thisis in marked contrast to the several logic gates per bit required forconventional incrementors. Therefore, significant savings in counteroverhead can be achieved by substituting LFSR-based counter 700 for theincrementor structure depicted in FIG. 6A, especially for simulatorsthat model incrementors utilizing logic gate based representations.

[0189] While the above described system and method provides a practicalmeans of instrumenting simulation models, in certain circumstancesadditional techniques maybe used in order to enhance the ease with whicha user may instrument a simulation model. In design, it often occursthat there are common design or instrumentation logic constructs thatare often repeated and possess a regular structure.

[0190] By utilizing knowledge of the regular structure of these designand instrumentation logic constructs, it is often possible to define asyntax that describes the instrumentation logic with considerablygreater efficiency than would be possible utilizing a conventional HDLconstruct. By utilizing this syntax as an unconventional HDL commentwithin a design VHDL file, it is possible to create instrumentationentities with considerably greater ease and efficiency.

[0191] Such comments within a design entity will be referred tohereinbelow as an embedded instrumentation entity comment while theinstrumentation logic created by such a comment will be referred to asan embedded instrumentation entity.

[0192] A common logic design construct is the so-called “finite statemachine”. A finite state machine typically consists of a number ofstorage elements to maintain the “state” of the state machine andcombinatorial logic that produces the next state of the state machineand its outputs. These constructs occur with great frequency in typicallogic designs and it is advantageous to be able to efficientlyinstrument these constructs.

[0193] A typical set of count and failure events for a finite statemachine includes counting the number of times a state machine cyclesfrom a given current state to some next state, counting the number offunctional cycles the state machine spends in each state, ensuring thatthe state machine does not enter an illegal state, and ensuring that thestate machine does not proceed from a current given state to an illegalnext state. This list of events is but one of many possible sets ofevents that can be used to characterize a finite state machine and isused in an illustrative manner only.

[0194] With reference now to FIG. 8A there is depicted a representationof an exemplary state machine 800. Exemplary state machine 800 consistsof five states, labeled S0, S1, S2, S3, and S4 respectively, and ninelegal state transitions between these states. In what follows, it isassumed that state machine 800 consists of three latches and a set ofcombinatorial logic to produce the next state function. It is furtherassumed that the states are encoded into the three latches following theusual and customary encoding for integers. That is to say, state S0 getsan encoding of 000_(bin), state S1 gets an encoding of 001_(bin) stateS2 gets and encoding of 010_(bin), and so on.

[0195] With reference now to FIG. 8B there is shown an exemplary designentity 850 referred to as entity FSM with instance name FSM, whichcontains one instance of state machine 800. Furthermore, a signal output801, “fsm_state(0 to 2)” contains a three bit signal directly connectedto the outputs of the three storage elements comprising the stateelements of state machine 800. A signal input 802, fsm_clock, applies aclocking signal that controls the storage elements for state machine800.

[0196] In order to instrument state machine 800, it would conventionallybe necessary to create an instrumentation entity VHDL file containingthe logic necessary to detect the desired state machine events and passthem through to count and fail events. Such an instrumentation entityfile with appropriate instrumentation entity descriptor comments wouldtypically require substantially more lines of code than the HDLdescription of the state machine itself. Such a circumstance isundesirable. However, in the case of a regular logic structure such as afinite state machine, it is possible to define a brief syntax thatcharacterizes the finite state machine without resorting to a separateinstrumentation VHDL entity.

[0197] With reference now to FIG. 8C there is illustrated an exemplaryHDL file 860 for generating design entity 850 with an embeddedinstrumentation entity for monitoring the behavior of FSM 800.Specifically, an embedded instrumentation entity comment 852 isillustrated. As depicted in FIG. 8C, embedded instrumentation entitycomment 852 comprises a number of distinct sections including: aprologue and embedded instrumentation name declaration 853, a statemachine clock declaration 859, a state element declaration 854, a statenaming declaration 855, a state element encoding declaration 856, astate machine arc declaration 857, and an epilogue 858.

[0198] Prologue and embedded instrumentation entity name declarationcomment 853 serves to declare a name that is associated with thisembedded instrumentation entity. This comment line also serves todelineate the beginning of an embedded instrumentation entity commentsequence.

[0199] As further depicted in FIG. 8C, declaration comment 853 assumes anon-conventional syntax of the form: “——!! Embedded TYPE: name”, wherein“——!! Embedded” serves to declare an embedded instrumentation entity,“TYPE” declares the type of the embedded instrumentation entity—FSM inthis case, and “name” is the name associated with this embeddedinstrumentation entity.

[0200] State machine clock declaration comment 859 is utilized to definea signal that is the clocking control for the finite state machine.

[0201] State element declaration comment 854 is utilized to specify thestate-machine state storage elements. This comment declares the storageelements or signal names that constitute the state-machine state. Instate machine 800, the signals fsm_state(0 to 2) constitute the statemachine state information.

[0202] State naming declaration comment 855 is utilized to declarelabels to associate with various states of the given state machine.These labels are utilized in state machine arc declaration comment 857when defining the legal state transitions within the given statemachine.

[0203] State element encoding declaration comment 856 is utilized todefine a correspondence between the state machine labels defined bystate naming declaration comment 855 and the facilities declared bystate element declaration comment 854. In the example shown, the labelsof comment 855 are associated by position with the encodings given incomment 856 (i.e., the state labeled “S0” has the encoding 000_(bin),the state labeled “S1” has the encoding 001_(bin), etc.).

[0204] State-machine arc declaration comment 857 defines the legal statetransitions within the state machine. The various transitions of thestate machine are given by terms of the form “X=>Y” where X and Y arestate machine state labels given by comment 855 and X represents aprevious state machine state and Y a subsequent state machine state.

[0205] Epilogue comment 858 serves to close the embedded instrumentationentity comment. The specific syntax and nature of the comments betweenthe prologue and embedded instrumentation name declaration and theepilogue will vary with the specific needs of the type of embeddedinstrumentation entity being declared.

[0206] Embedded instrumentation entity comment 852 is inserted withinthe VHDL file of the design entity that contains the finite statemachine in question. The embedding of instrumentation for finite statemachine 800 is made possible by the non-conventional comment syntaxillustrated in FIG. 8C and is substantially more concise than aconventional HDL instrumentation entity suitable for accomplishing thesame function.

[0207] Utilizing such embedded non-conventional comments, the system ofthe present invention creates an instrumentation entity, as describedbelow, for instrumenting the state machine without the need to resort tocreating a separate HDL file instrumentation entity.

[0208] To support compilation and creation of embedded instrumentationentities, the previously described compilation process of FIG. 4D isenhanced as described herein. First, HDL compiler 462 is altered torecognize the presence of embedded instrumentation entity comments. If,during compilation of a design HDL file, and subject to the constraintsdescribed above for incremental compilation, HDL compiler 462 detectsone or more embedded instrumentation entity comments within the sourcecode file, HDL compiler 462 places a special marker into design entityproto data structure 463.

[0209] When instrumentation load tool 464 is passed control, proto datastructures 463 are searched in order to locate the special marker placedby HDL compiler 462 indicating embedded instrumentation entity comments.Such protos represent the design HDL files with embedded instrumentationentities that have been re-compiled in the current compilation cycle.

[0210] When instrumentation load tool 464 locates a proto data structure463 with the special marker, the corresponding VHDL source code file forthe design entity is opened and parsed to locate the one or moreembedded instrumentation entity comments. For each of these comments,instrumentation load tool 464 creates a specially named proto datastructure 463 a, and further generates a corresponding instance datastructure 465 a that is instantiated within the design entity. Inaddition, instrumentation load tool 464 removes the special markerinserted by HDL compiler 462 to prevent unnecessary re-instrumentationof the design proto on subsequent re-compiles.

[0211] Within these created embedded instrumentation entity protos,instrumentation load tool 464 directly creates the necessaryinstrumentation logic required by the embedded instrumentation entitywithout the need for a VHDL file to specify this instrumentation andconnects this logic to instrumentation logic block 420 of FIG. 4D. Theupdated design proto along with the embedded instrumentation entityproto and instance data structure are saved to disk and serve as inputsto subsequent compiles, removing the need to produce embeddedinstrumentation entities on subsequent recompiles.

[0212] With reference now to FIG. 9, design entity 850 is showninstrumented with embedded instrumentation entity 900. Embeddedinstrumentation entity 900 is created as a proto instantiated withindesign entity 850 wherein the embedded non-conventional instrumentationentity comment occurs. The embedded instrumentation entity thus may bereplicated automatically within an overall design wherever the specificdesign entity is instantiated.

[0213] Embedded instrumentation entity 900 is named in a unique mannerbased on the name associated with the embedded instrumentation entity bythe prologue and embedded instrumentation name declaration comment. Thisname is pre-pended with a special character (shown as a “$” in FIG. 9)that is not a recognized naming convention for the platform HDL. In thismanner, the names of the embedded instrumentation entities cannotconflict with the names of any other design or standard instrumentationentities.

[0214] Furthermore, the names associated with the various events definedby the embedded instrumentation entity (the “varname” for the countevents, for example) are also derived in a fixed manner from the nameassociated with the embedded instrumentation entity. The user isrequired to ensure that the names of embedded instrumentation entityevents do not conflict with the names of standard instrumentation entityevents and further than the names of the embedded instrumentationentities within a given design do not themselves conflict.

[0215] It should also be noted that if a design entity contains morethan one embedded instrumentation entity, the embedding processdescribed with reference to FIG. 8B and FIG. 9 is simply repeated foreach such instrumentation entity. In addition, since the protos for theembedded instrumentation entities are created at the same time as thedesign protos itself, no changes to the BOM mechanism used forincremental compiles are required. The protos for the embeddedinstrumentation entities can be considered, for purposes of incrementalcompilations, to be mere extensions to the design proto itself.

[0216] The present invention discloses a method and system for namingevents within a simulation model that prevents name collisions betweenevents in different instrumentation entities, allows for the arbitraryre-use of components of a model in models of arbitrarily increasingsize, and furthermore allows for processing designated events in ahierarchical or non-hierarchical manner.

[0217] When all instances of an event are considered as a whole withoutregard to specific instances, the event is considered in a“non-hierarchical” sense. Likewise, when an event is considered withregard to each and every instance, it is considered in a “hierarchical”sense. When considering count events, for example, it is oftenconvenient to track the number of times a particular count eventoccurred in the aggregate without concern to exactly how many times thecount event occurred in each particular instance within a simulationmodel.

[0218] Each type of event: count, fail, and harvest, is given a separateevent namespace by construction. Each event class is therefore anindependent group preventing naming collisions between the event types.The data structure of the present invention is independently applied toeach of the different event types to ensure correctness within eachevent class.

[0219] In the embodiments illustrated in FIGS. 10A, 10B, 10C, and 10D,the system and method of the present invention are described withrespect to count events. One skilled in the art will appreciate andunderstand the extensions necessary to apply the same techniques toother event classes such as failures or harvests.

[0220] With reference to FIG. 10A, there is depicted a block diagramrepresentation of simulation model 1000 containing a number of designand instrumentation entities. As illustrated in FIG. 1A, simulationmodel 1000 includes two instances of a design entity X, with instancenames X1 and X2 respectively.

[0221] Within each of design entity instances X1 and X2 is instantiatedan instance of an instrumentation entity B3, 1012 a and 1012 b. Designentity instances X1 and X2 further comprise instances, 1014 a and 1014b, respectively, of design entity Z which further contains instances,1016 a and 1016 b, of instrumentation entity B1 and instances, 1018 aand 1018 b, of instrumentation entity B2.

[0222] Finally, simulation model 1000 includes an instance of designentity Y, with instance name Y, containing an instance ofinstrumentation entity B4 1022. Design entity instance Y contains aninstance, 1024, of design entity Z with further instances, 1016 c and1018 c, of instrumentation entities B1 and B2 respectively.

[0223] In what follows the methods of the present invention for uniquelynaming events will be considered in the context of exemplary model 1000.It will be assumed in the following description that eachinstrumentation entity (B1, B2, B3, and B4) has declared a single countevent with event name “count1”.

[0224] In accordance with the method and system of the presentinvention, the user must uniquely name each type of event (count, fail,or harvest) within a specific instrumentation entity, i.e., the usercannot declare any two events of the same type within the sameinstrumentation entity with the same event name. Such a constraint doesnot conflict with the stated goals of the present invention in that agiven instrumentation entity is usually created by a specific person ata specific point in time, and maintaining unique names within such alimited circumstance presents only a moderate burden to the user. Thedata structure disclosed herein does, however, prevent all namecollisions between events in different instrumentation entities, andallows for processing the events in a hierarchical and/ornon-hierarchical manner.

[0225] As previously explained, an HDL naming convention must uniquelyidentify all the entities within a given design. This constraint isinherent to HDLs and applies to design entities as well asinstrumentation entities. In accordance with conventional VHDL entitynaming constructs, it is technically possible for two design entities toshare the same entity name, entity_name. However, such identically namedentities must be encapsulated within a VHDL library from which a validVHDL model may be constructed. In such a circumstance, entity_name, asit is utilized herein, is equivalent to the VHDL library nameconcatenated by a period (“.”) to the entity name as declared in theentity declaration.

[0226] Pre-pending a distinct VHDL library name to the entity namedisambiguates entities sharing the same entity name. Most HDLs include amechanism such as this for uniquely naming each design entity. Designentities must be unambiguously named in order to determine whichparticular entity is called for in any given instance in a simulationmodel. The present invention employs the prevailing naming mechanism ofthe native HDL to assign unique entity names for design entitiesthroughout a given model and leverages the uniqueness property of entitynames and the uniqueness of each instance's instantiation identifier tocreate an “extended event identifier” for each event within thesimulation model.

[0227] With reference to FIG. 10B, there is illustrated a representationof the fields in an extended event identifier data structure,alternatively referred to herein as an “event list”, in accordance withone embodiment of the present invention. The extended event identifierbegins with instantiation identifier field 1030. This field, asdescribed hereinbefore, consists of the instance identifiers, proceedingfrom the top level entity to the direct ancestor of the given instancewithin the simulation model separated by periods (“.”). This string isunique for each and every instance of the event within the model. Theextended event identifier further includes an instrumentation entityfield 1032, a design entity field 1034, and an eventname field 1036.

[0228] Instrumentation entity field 1032 contains the name of theinstrumentation entity (or the name assigned to an embeddedinstrumentation entity) that generates the simulation event. Designentity field 1034 contains the entity name of the design entity in whichthe event occurs. Eventname field 1036 is the name given to the event inthe instrumentation entity description comments of an instrumentationentity or the event name assigned to an event within an embeddedinstrumentation entity. These four namespace fields comprise a uniqueidentifier for each event within a simulation model that allows for there-use of components within other models without risk of name collisionsand the consideration of events in a hierarchical or non-hierarchicalsense.

[0229] With reference now to FIG. 10C, there is shown a list of extendedevent identifiers for model 1000. Event identifiers 1040, 1041, 1042,1043, 1044, 1045, 1046, 1047, and 1048 are declared within simulationmodel 1000 to designate count events having eventname “count1”. Theextended event identification procedure of the present invention will bedescribed in the context of these extended event identifiers.

[0230] The uniqueness of the names in design entity name field 1034 is aprimary distinguishing factor between events. By including the designentity name in the extended event identifier, each design entity is, ineffect, given a unique namespace for the events associated with thatdesign entity, i.e., events within a given design entity cannot havename collisions with events associated with other design entities.

[0231] It is still possible however, to have name collisions betweenevents defined by different instrumentation entities that areincorporated within a single design entity. Events 1041 and 1042, forexample, if identified solely by the design entity name, have a namecollision. Both are events with eventname “count1” within design entityZ, and if labeled as such, are indistinguishable. In order to alleviatea naming collision between events 1041 and 1042, the present inventionemploys instrumentation entity field 1032. By referencing the designentity and instrumentation entity names, both of which are unique withrespect to themselves and each other, a unique event namespace iscreated for each instrumentation entity associated with any given designentity. For example, event identifier 1041 and 1042 would be in conflict(both named Z.count1), unless the respective instrumentation entitynames are included within the extended event identifier to produce namesB1.Z.count1 and B2.Z.count2 for these events.

[0232] It should be noted that it is possible to uniquely name eachevent by using instrumentation entity name field 1032 alone. Due to theuniqueness property of instrumentation entity names, event names thatare only named by the instrumentation entity name and the event namefield will be necessarily unique.

[0233] However, such a naming scheme is insufficient for associatingevents with a given design entity. In practice, it is desirable toassociate events with the design entity in which they occur rather thanassociating them with the potentially numerous instrumentation entitiesthat are utilized to track them. Moreover, referencing the appropriatedesign entity within the eventname allows all the events associated witha given design entity to be centrally referenced without the need toascertain the names of all the instrumentation entities associated withthe given design entity. The data structure of the present inventionutilizes both the instrumentation entity and design entity names innaming events for ease of reference at the cost of moderate uniquenessredundancy in the event names.

[0234] In an alternative embodiment of the present invention, theinstrumentation entity name is not included within the extended eventidentifier. Referring to FIG. 10D, such an alternative extended eventidentification data structure is depicted. As shown in FIG. 10D, eventsare named by instantiation identifier field 1030, design entity namefield 1034, and event name field 1036.

[0235] Such a data structure provides name collision protection betweendesign entities but not within design entities. That is, the user mustensure that events names for events associated with a given designentity do not collide. In case of user error in this regard, model buildtools may be utilized to detect an event name collision condition duringmodel compilation. The alternative data structure depicted in FIG. 10Dprovides for simpler naming and referencing of events at the expense ofrequiring the user to prevent name collisions for events associated witha given design entity.

[0236] Returning to FIG. 10B, the combination of instrumentation entityfield 1032, design entity name field 1034, and eventname field 1036 fora given event, provides a unique identifier for any given event withoutregard to multiple instantiations of the event. In order to uniquelydistinguish between multiple instantiations of an event, instantiationidentifier field 1030 is included in the extended event identifier.Instantiation identifier field 1030 field, by its construction, providesa unique string for any instance of an entity within any simulationmodel.

[0237] When evaluating occurrences of an event in a non-hierarchicalsense, instantiation identifier field 1030 is ignored while searchingfor matching events. As illustrated in FIG. 10C, for example, anon-hierarchical query for the number of time a “count1” event occurswithin design entity Z as detected by instrumentation entity B1,utilizes the following list of count eventnames: X1.Z B1 Z COUNT1 X2.ZB1 Z COUNT1 Y.Z B1 Z COUNT1.

[0238] These count events are added together to form an aggregate countof the total number of time the specific event occurred within thesimulation model.

[0239] A hierarchical query includes specific criteria to match againstthe hierarchy field to limit the counter or counters found to specificinstances of the requested event. For example, a query to obtain thecount1 event of instrumentation entity B1 within the X1.Z instance ofdesign entity Z utilizes the following count eventname: X1.Z B1 ZCOUNT1,

[0240] which represents the number of times the count1 event was countedby instrumentation entity B1 within design entity instance X1.Z for aparticular simulation interval.

[0241] By providing matching model hierarchy criteria againstinstantiation identifier field 1030, it is possible to consider theevents with respect to their particular instance or instances within themodel, i.e., a hierarchical query. A non-hierarchical query merelyignores the hierarchy field and returns all the instances of therequested events within the model.

[0242] With reference to FIG. 11A, there is depicted a block diagramillustrating a simulation model 1100 in which the hierarchical eventprocessing of the present invention is applicable. Simulation model 1100comprises atop-level design entity 1130 in which a pair of lower-leveldesign entities 1102 and 1120 are instantiated. A design entity 1104containing instrumentation entity 1106 is included within design entity1102. As illustrated in FIG. 11A, instrumentation entity 1106 includeslogic 1110 for generating a simulation event 1108 from signal set 1132from within design entity 1104. Design entity 1120 includes aninstrumentation entity 1122 that generates a simulation event 1124 usingsignal set 1134.

[0243] Utilizing the techniques described hereinbefore, generating ahierarchical event that is some logical combination of events 1108 and1124 requires the creation of an instrumentation entity associated withtop level design entity 1130 that references signal sets 1132 and 1134.Conventionally, such an instrumentation entity would substantiallyreproduce instrumentation logic 1110 and 1126 to process signal sets1132 and 1134, respectively, thus producing a copy of events 1108 and1124. Such a procedure is inefficient and prone to error. If, forexample, changes are made to any or all of signal sets 1132 and 1134, orinstrumentation logic 1110 and 1126, these changes would have to beaccurately repeated in the instrumentation entity logic for the combinedevent.

[0244] The present invention provides a mechanism whereby events, suchas events 1108 and 1124, are directly referenced and utilized as inputsto cross-hierarchical instrumentation entities. In this manner, signalconnections 1132 and 1134, as well as instrumentation logic 1110 and1126, are directly re-utilized to produce the desired hierarchicalevent.

[0245] To facilitate direct referencing of events within simulationmodels, a specialized data structure is implemented withininstrumentation entity input port map comment syntax. This datastructure directly connects input ports of instrumentation entities tocross-hierarchical events within a simulation model.

[0246] For the embodiment depicted in FIG. 11A, an instrumentationentity 1150 is instantiated within top-level design entity 1130 togenerate a hierarchical event 1156 that is some function of events 1108and 1124. As illustrated in FIG. 11A, instrumentation entity 1150includes a pair of inputs 1151 and 1152 that are directly connected toevents 1124 and 1108, respectively, utilizing the augmented syntaxdescribed below. These input connections are logically combined usinginstrumentation logic 1154 to produce a cross-hierarchical event 1156.

[0247] With reference to FIG. 11B, there is depicted a set of input portmapping comments for performing cross-hierarchical processing ofsimulation model events in accordance with the teachings of the presentinvention. In what follows, it is assumed that events 1108 and 1124 arecount events with event names event_(—)1108 and event_(—)1124,respectively, and that these events are connected to input portsevent_(—)1108_in and event_(—)1124_in on instrumentation entity 1150. Asdepicted in FIG. 11B, a first input port mapping comment 1161 containsdata for referencing event 1108 to input port event_(—)1108_in. A secondinput port mapping comment 1162 contains data for referencing event 1124to input port event_1124_in. It should be noted that each of input portmapping comments 1161 and 1162 includes a pre-pended non-conventionalcomment identifier, ——!!, that is utilized by the HDL compiler (such ascompiler 462 in FIG. 4D) to maintain the port mapping comments separatefrom the design.

[0248] To facilitate connection of a simulation event to aninstrumentation entity input port, input port mapping comments 1161 and1162 consist of two distinct parts: an instance identifier and an eventidentifier. The instance identifier is a string consisting of instancenames (in descending hierarchical order) of all design entities betweenand including the design entity containing the instrumentation entity ofthe cross-hierarchical event being defined (i.e., the highest leveldesign entity for the cross-hierarchical event), and the design entityin which the event that is utilized in generating the cross-hierarchicalevent. If the design entity containing the hierarchical event is thesame as the design entity containing the event to be connected to, theinstance identifier is a null string. A pair of instance identifiers1163 and 1164, within input port mapping comments 1161 and 1162,respectively, specify that events 1124 and 1108 originate from signalswithin design entity 1120 and 1104 respectively.

[0249] Input port mapping comments 1161 and 1162 further include eventidentifiers 1165 and 1166, that identify input simulation events interms of local instrumentation entities 1106 and 1122, respectively. Inaccordance with the embodiment depicted in FIG. 11B, each eventidentifier consists of a string beginning with an open bracket (“[”)character and ending with a closed bracket (“]”) character. Betweenthese brackets, three sub-strings, delineated by period (“.”)characters, comprise a data structure utilized to identify a specificevent from which the cross-hierarchical event is defined. The firstsub-string within an event identifier is the instance name of theinstrumentation entity containing the event. The second sub-string is astring specifying the type of the event (“count”, “fail”, or “harvest”).Finally, the third sub-string is the event name of the given event asspecified in the declaration comment for the event. Each eventidentifier string uniquely identifies a single event within a givendesign entity. As depicted in FIG. 11B, event identifier strings 1165and 1166 identify events 1108 and 1124 respectively.

[0250] In accordance with an alternate embodiment of the presentinvention, the event identifier naming structure is modified slightlyfor events that are labeled in accordance with FIG. 10D (event namesthat do not include the instrumentation entity name). When aninstrumentation identifier is absent from the extended event identifier,the event identifier string with an input port mapping comment consistsof two sub-strings: a string denoting the type of event to connect to;and a string providing the name of the event separated by a period (“.”)character. The instrumentation entity name is not required in this casesince all events of a given type associated with a given design entitywill have unique names. The model build tools of the present inventionwill automatically search all instrumentation entities associated withthe design entity called out by the instance identifier to determinewhich instrumentation entity generates an event having the name and typeprovided in the event identifier string.

[0251] Referring to FIG. 11C, there is illustrated a set of datastructures for performing hierarchical processing of simulation modelevents in accordance with a second embodiment of the present invention.In the depicted embodiment, a pair of input port mapping comments 1171and 1172 employ a syntax compatible with the event naming data structuredepicted in FIG. 10D.

[0252] Input port mapping comment 1171 connects event 1108 to input portevent_1108_in on instrumentation entity 1150. Likewise, input portmapping comment 1172 connects event 1124 to input port event_1124_in oninstrumentation entity 1150. By utilizing the augmented syntax of FIG.11B or FIG. 11C it is possible to create hierarchical events byconnecting the inputs of instrumentation entities to events within thesimulation model.

[0253] The above described system and method provides for practicalinstrumentation of simulation models and allows for efficientimplementation of instrumentation logic through embedded instrumentationentities. Embedded instrumentation entities, as described hereinabove,are however necessarily limited to task-specific implementations. Asdescribed with reference to FIGS. 12A and 12B, the present inventionfurther provides for a more flexible implementation of instrumentationlogic in a more unstructured manner.

[0254] It is often necessary to tailor instrumentation logic to addressunique problems and circumstances. Instrumentation logic of a specificand yet non-predefined nature that is designed in accordance with thetechniques disclosed herein with reference to FIGS. 12A and 12B isreferred herein as “random instrumentation logic.” A data constructconsisting of general logic primitives (boolean operators, storageelements, etc.) and an interconnection method for these primitives isutilized for implementing such random instrumentation logic.

[0255] For instrumenting a simulation model as described heretofore, anHDL such as VHDL or Verilog is utilized as a platform from whichinstrumentation logic is generated. Appropriate instrumentation entitydescriptor comments within design entity source code files couple theresultant instrumentation entities to designated target design entitieswithin a simulation model.

[0256] In addition to entity descriptor comments within a design entitysource code file, the foregoing instrumentation technique requires aseparate HDL file in which the instrumentation entity is described. Asexplained with reference to FIGS. 12A and 12B, the present inventionprovides a method, system, and data structure for instrumenting designentities within a simulation model while avoiding the design processoverhead required for creating a separate instrumentation entity HDLfile.

[0257] In accordance with the teachings of the present invention, randominstrumentation logic is directly deployed within target design entitiesin terms of individualized and customizable instrumentation descriptorcomments. Such instrumentation descriptor comments are encoded withinthe target design entity HDL source code file and provide a means forthe describing random instrumentation logic, events, andinterconnections between the created instrumentation logic and thetarget design entity. The random instrumentation logic is inserted intothe simulation model in a manner similar to the techniques used forembedded instrumentation entities to produce an instrumentation entitywithout the need for the creation of an explicit HDL instrumentationentity file.

[0258] With reference to FIG. 12A, there is illustrated a representativetarget design entity 1200 wherein random instrumentation logic isimplemented in accordance with a preferred embodiment of the presentinvention. Instantiated within target design entity 1200 is a designentity 1201. As further depicted in FIG. 12A, an instrumentation entity1202 is instantiated within design entity 1201. Instrumentation entity1202 is designed in accordance with the principles set forth hereinaboveto generate a count event 1203 having an event name “count1.” Targetdesign entity 1200 further includes an instrumentation entity 1208 thatis generated utilizing random instrumentation logic. As depicted in FIG.12A, instrumentation entity 1208 receives as inputs signals P, A, B, andC along with count event 1203.

[0259] Instrumentation entity 1208 is constructed by a set ofunconventional comments lines within the source code file for targetdesign entity 1200. These comments may be incorporated at any pointwithin the logic description section of the HDL source code file. HDLcompiler 462 (FIG. 4B) recognizes the unconventional comments inaddition to any comments utilized to instantiate embeddedinstrumentation entities within design entity 1200. During thepost-compilation/model build phase, instrumentation load tool 464processes these comments in a manner similar to that utilized forembedded instrumentation entities (described with reference to FIGS.10A-10D) to generate instrumentation entity 1208.

[0260] A variety of possible syntaxes can be utilized to formulate theunconventional HDL comments required for generating randominstrumentation logic within the source code file of a target designentity. As depicted in FIG. 12B, much of the syntax of these commentsemploys syntax similar to the concurrent subset of the VHDL languagewith the addition of syntactic and semantic enhancements that provide ameans of connection between an instrumentation entity and its targetdesign entity. In addition, minor syntactic and semantic enhancementsare provided to declare events and intermediate signals.

[0261] With reference now to FIG. 12B, there is illustrated an exemplaryHDL source code file 1220 that describes design entity 1200. Within HDLsource code file 1220, an entity instantiation 1221 produces designentity 1201, and assignment statements 1222 are utilized to generatesignals A, B, and C. A set of unconventional comments 1223 within HDLsource code file 1220 is utilized to produce instrumentation entity1208. Comments 1223 are formulated as left-hand side (l.h.s.)/right-handside (r.h.s.) assignment statements of the form:

[0262] {l.h.s.}<={r.h.s.};

[0263] where {l.h.s.}, referred to herein after as lhs, is theassignment statement target and, {r.h.s}, referred to herein after asrhs is an expression denoting the logical value to be assigned to thestatement lhs. A number of rules delineate the possible expressions forlhs and rhs in any legal statement in the instrumentation comments.

[0264] As employed within the instrumentation data structure of thepresent invention, an lhs statement may be either an event declarationor the name of a signal that is instantiated within an instrumentationentity. An event declaration is an expression within bracket characters(“[”, “]”) that generates a new event. Within comments 1223, a statement1230 produces a count event 1240 from instrumentation entity 1208 (FIG.12A) having eventname “countname0”.

[0265] Within an lhs event declaration, a first field designates theevent type (count, fail, harvest, etc.) and is followed by such otherfields as are necessary to declare the event. As illustrated in lines1230, 1234, and 1236, such event declaration fields follow the sameformat as the event declaration fields depicted in FIG. 4C.

[0266] Comments 1223 further include a line 1232 having an lhs thatdeclares a signal Q within instrumentation entity 1208. To preventambiguity, any signal declared in this manner may not have a namecorresponding to the name of any signal present on the top level oftarget design entity 1200. Conformance to this requirement is verifiedby instrumentation load tool 464 (FIG. 4D) during processing. Signalsdeclared by an lhs expression may be incorporated within an rhsexpression as shown in lines 1232 and 1234.

[0267] An rhs consists of logical connectivity expressions and/orfunctions that combine various signals. Signals within theseconnectivity expressions may originate from a number of possible sourcesincluding: signals declared on the lhs of a statement in theinstrumentation comments; signals within the target design entity; orsignals designating other events within the target design entity.

[0268] The absence of period (“.”) or bracket (“[”, “]”) characterswithin a signal value description in the rhs of a statement, designatesthe object signal as corresponding to either a signal within the tophierarchical level of the target design entity or to a signal declaredon the lhs of a statement within the instrumentation language. Signalsare named in a mutually exclusive manner by the rules governing creationof signals on the lhs of a statement in the instrumentation comments,thereby preventing any ambiguity in the determining the source of thegiven signal.

[0269] Signals in rhs connectivity expressions can also be connectionsto signals within entities instantiated within the target design entity.In such a circumstance, the instance names of the entity or entities inthe hierarchy enclosing the desired signal are placed before the signalname in hierarchy order, delineated by period (“.”) characters. Forexample, the signal in statement 1230 (“Y.P”) represents signal 1204within design entity 1201. Signals at any level of the target designhierarchy are thus accessible to instrumentation logic generated by theinstrumentation language comments.

[0270] Signals within the instrumentation comment expressions can alsodesignate other events within the target entity. Event identifiers asdescribed hereinbefore for hierarchical events are used to denote such“event” signals. For example, statement 1232 performs a logical AND ofinstrumentation event 1203 and signal A. The event identifier“Y.[B1.count.count1]” connects instrumentation entity 1208 toinstrumentation event 1203. This notation permits instrumentation eventsat any level of design hierarchy within target design entity 1200 to bedirectly accessed.

[0271] As further depicted in FIG. 12B, statement 1232 producesintermediate signal Q within instrumentation entity 1208. This is anexample of an instrumentation comment statement declaring a newintermediate signal. These signals can be used in other statements toconstruct random instrumentation logic of any desired depth orcomplexity.

[0272] Statement 1234 utilizes intermediate signal Q along with signal1206 to produce fail event 1241. The syntax for fail event declarationincludes a field denoting the type of event (“fail”), a field giving theevent name for the fail event (“failname0”), and a final field denotingthe message to associate with the fail. Finally, statement 1236 producesharvest event 1242.

[0273] In general, the rhs expression of any statement in theinstrumentation data structure of the present invention can access anysignal or instrumentation event signal within the target design entityutilizing these syntactic mechanisms. These signals can be combined toform new events or intermediate signals that can themselves be furthercombined to form instrumentation logic of any desired depth orcomplexity.

[0274] Instrumentation comments can be placed anywhere within the logicdescription section of the target entity source code file. Allinstrumentation comments within a file are considered as a whole andproduce a single instrumentation entity within the target design entity.

[0275] It is often necessary to override signal values within asimulation model to test various functions and create certain conditionsthat would otherwise not be possible or simple to obtain. To provide anefficient and designer accessible means of overriding signal valueswithin a simulation model, the present invention incorporatesspecialized signal override output ports and logic into instrumentationentities that permit bus signal overrides during model simulation. Suchsignal override output ports must have different names that the outputsfor count, fail, and harvest events, and compliance with this conditionis verified by instrumentation load tool 464 (FIG. 4D) during the modelbuild process.

[0276] The signal override output ports may be declared explicitly asports in the HDL source code file of an explicitly representedinstrumentation entity. For an embedded instrumentation entity orinstrumentation entities produced by random instrumentation logiccomments within a target design entity, the signal override output portsare automatically generated by instrumentation load tool 464. The signaloverride output ports are described in output port map statements thatdeclare an alternate value that overrides a given simulation signal.Such an output port map statement further declares the conditions underwhich the simulation signal will be overridden. For each simulationsignal (single or multi-bit) to be overridden, two output signals areproduced: one providing the override signal value and another in theform of a single bit signal that enables or disables overriding of thesimulation signal.

[0277] With reference to FIG. 13A, there is depicted an exemplary HDLdesign entity 1300 containing a multi-bit simulation signal R.Simulation signal R is driven by one or more sources within a logicmodule 1302, and is received by one or more sinks within a logic module1304. While the present invention will be described with respect tooverriding signal R, one skilled in the art will understand andappreciate the extensions necessary to apply the principles set forthherein to overriding single bit signals or subsets of multi-bit signals.

[0278] Referring to FIG. 13B, signal override functionality isincorporated within design entity 1300. As illustrated in FIG. 13B,design entity 1300 includes an instrumentation entity 1306 that isequipped to override signal R. In the depicted embodiment,instrumentation entity 1306 produces a signal R_OV(0 . . . 4) that isutilized within HDL design entity 1300 to selectively replace signal Ras an input into logic module 1304. Instrumentation entity 1306 furtherproduces a signal RT that enables an override of signal R with signalR_OV(0 . . . 4).

[0279] During a model build process, instrumentation load tool 464,instantiates a multiplexor (MUX) 1308 that breaks the path of theoriginal signal R as depicted in FIG. 13A to produce signal R′ that isinput into logic module 1304. MUX 1308 is directly instantiated byinstrumentation load tool 464 into the design proto data structure forinstrumentation entity 1306 without the need to alter the HDL sourcecode for HDL design entity 1300. MUX 1308 selects between overridesignal R_OV(0 . . . 4) and the original signal value R to determine thevalue of signal R′.

[0280] MUX 1308 is controlled by a select signal 1310 that is driven bylogical AND gate 1312. Logical AND gate 1312 is driven by signal RT anda latch bit signal from a latch 1314. In a manner similar to thatdescribed in relation fail events in FIG. 5A, latch 1314, when loadedwith a binary ‘1’ value, forces MUX 1308 to select the original signalvalue R, thereby disabling overrides of signal R.

[0281] Instrumentation load tool 464 generates latch 1314 and logicalAND gate 1312 for every overriddable signal within the simulation model.All signal overrides within a model can thus be selectively andindividually disabled. For each overriddable signal, latch 1314 resideswithin instrumentation logic block 420 as depicted in FIG. 4B. Inaccordance with a preferred embodiment, only one instrumentation entitymay override a given single bit signal or any signal or subset ofsignals within a multi-bit signal.

[0282] The signal override system of the present invention furtherprovides a means by which instrumentation entities may access theoriginal unaltered version of any given signal within the simulationmodel. As depicted in FIG. 13B, for example, instrumentation entity 1306accesses the unaltered signal R by means of input signal 1320.

[0283] With reference to FIG. 13C, there is illustrated an exemplary HDLsource code file 1340 that describes instrumentation entity 1306. HDLsource code file 1340 includes entity descriptor comments 1351 and anarchitecture section 1358 comprising the functional logic withininstrumentation entity 1306.

[0284] Within HDL source code file 1340, an input port map statement1364 declares an input port 1309 at which instrumentation entity 1306receives signal R from logic module 1302. A set of output port mapstatements 1362 and 1363 define the output ports from instrumentationentity 1306 for signals R_OV(0 . . . 4) and RT, respectively.

[0285] An input port map comment 1360 connects signal input 1320 toinstrumentation entity 1306. Input port map comment 1360 employs anaugmented syntax from that previously described for input port mapcomments. Signals that appear within brace (“{”, “}”) characters aredefined to reference the original unaltered version of a signal within aparticular target design entity. Hence the statement

[0286] ——!! R_IN(0 to 4)=>{R(0 to 4)}

[0287] connects input port R_IN to signal R in the altered design protodata structure. The brace syntax may be used to enclose any signalwithin a port map comment or any signal referred to within a randominstrumentation comment statement RHS. If a signal referenced withinbraces has no associated override mechanism in the model, the signaldeclaration within braces merely resolves to the unaltered signalitself.

[0288] An additional comment section, output declarations 1361, isfurther included within the descriptor comment syntax to declare signaloverrides. Output declaration comment 1361 serves to generate theoverride logic shown in FIG. 13B and connect signals RT and R_OV(0 . . .4) to the override logic. Output declarations such as output declarationcomment 1361 are of the form:

[0289] ——!!<name>:out_port=>target_signal [ctrl_port];

[0290] where name is a name associated with the specific signal override(R_OVRRIDE in FIG. 13C), out_port is the output port providing theoverride value for the signal, target_signal is the name of the signalto be overriden, and ctrl_port is the single bit output port thatdetermines when the target signal is overridden.

[0291] One such output declaration is required for every overridablesignal. Signals designated by out_port and ctrl_port must appear asoutputs declared in the HDL port map for the entity. As it does forinstrumentation entities, instrumentation load tool 464 parses thecomments depicted in FIG. 13C and alters the proto data structureswithin the simulation model at model build time to produce a result suchas that depicted in FIG. 13B.

[0292] Furthermore, each signal override is given a unique name based onthe “name” field within the output declaration comment in a manneranalogous to that described earlier for count events. In this manner,each signal override can be specifically referred to for such purposesas disabling the signal override by setting latch 1314 as shown in FIG.13B.

[0293] While FIG. 13C illustrates only those constructs necessary forimplementing a signal override, it should be noted that there is nolimitation placed upon the creation of count, fail, and harvest eventswithin the same instrumentation entity as a signal override, andfurthermore, that multiple signal override entities may be incorporatedwithin the same instrumentation entity.

[0294] Referring to FIG. 13D, there is depicted an HDL source code filefor producing design entity 1300 wherein a set of random instrumentationcomments 1380 implement the logic necessary for selectively overridingsignal R. A comment 1381 connects the unaltered version of signal R(referred to within the braces (“{”, “}”) syntax) to an internalinstrumentation signal R_IN. A pair of comments 1382 and 1383 assignvalues for signals R_OV and RT (the exact expressions assigned to RT andR_OV are not depicted). An event declaration instrumentation comment1384 produces a signal that enables signal R to be overridden. Inaccordance with the depicted embodiment, an event declaration comment isof the form:

[0295] [override, <name>, <target_signal>, <ctrl_signal>],

[0296] where override is a fixed string denoting a signal override,“<name>” is the name assigned to the override, “<target_signal>” is thename of the signal in the target entity to be altered, and“<ctrl_signal>” is the name of the signal that determines when thesignal override takes effect. By utilizing random instrumentationcomments, a design engineer can efficiently create signal overrides.

[0297] Since in accordance with the teachings of the present invention,signal overrides are implemented directly using hardware constructs, itis possible to efficiently utilize signal overrides within a hardwaresimulator.

[0298] Simulation of a given model is typically controlled by a program,hereinafter referred to as RTX (Run Time eXecutive), that is written ina high-level language such as C or C++. To facilitate RTX control andexecution of a simulation run, simulators typically provide a number ofapplication program interface (API) functions that may be called by theRTX. Such API functions employ routines, protocols, and tools that allowfor polling of signals within the simulation model, alteration ofsignals within a simulation model, cycling a simulation model, etc.

[0299] The RTX is often required to monitor occurrences of significantevents during model simulation. Such events typically consist of asignal or a set of signals that assume a prescribed sequence of valuesand will be referred to hereinafter as “model events.”To monitor modelsignals, an RTX typically calls a specialized API function, hereinafterreferred to as GETFAC, which allows for polling of signal values duringmodel simulation.

[0300] Typically, an RTX must monitor a large number of signals,potentially on a cycle-by-cycle basis, within a simulation model andmust subsequently process the signal values in order to detect theoccurrence of model events. This approach to model event monitoringplaces a burden on the verification engineer in terms of re-writing RTXand communicating with design engineers when the signals or sequence ofsignal values that constitute a model event change.

[0301] To provide an efficient means for monitoring model events,so-called “detection events” are generated and are accessible by theRTX. Such detection events are generated by instrumentation entities.Detection events are implemented as output ports on an instrumentationentity. Furthermore, an enhanced API function is provided for directlyaccessing detection events within a given simulation model.

[0302] For each detection event, a first, potentially multi-bit, outputis utilized as the value of a model event. An optional second single bitsignal is utilized to indicate when the detection event occurs duringmodel simulation. By their nature, certain model events occur at eachand every cycle and therefore do not require a qualifying signal totrack their occurrences.

[0303] With reference to FIG. 14A, there is illustrated a block diagramdepicting data content within main memory 44 (FIG. 2) during asimulation run of a simulation model 1400. Main memory 44 includes theelements necessary for monitoring an exemplary model event including asoftware simulator 1410 that simulates simulation model 1400 under thecontrol of an RTX 1405.

[0304] RTX 1405 delivers a set of API calls 1430 to API function GETFACwithin simulator 1410 to obtain the values of signals A, B, C, and Dwithin model 1400. Further processing of these signal events isperformed utilizing an RTX code 1450 culminating in the assignment ofthe model event value to variable event_x at RTX code line 1455.

[0305] Referring to FIG. 14B, there is illustrated a block diagramdepicting contents of main memory 44 during a simulation run inaccordance with a preferred embodiment of the present invention. In thedepicted embodiment, an instrumentation entity 1460 is instantiatedwithin simulation model 1400 using techniques described above.Instrumentation entity 1460 directly monitors signals A, B, C, and D bymeans of a set of signal connections 1462. Signal connections 1462provide a more efficient means to monitor signals than GETFAC APIfunction calls.

[0306] Within instrumentation entity 1460, instrumentation logic 1464substantially recreates the function of RTX code 1450 of FIG. 14A andproduces signals 1466 and 1468, which denote the value of a model eventand when the model event occurs, respectively.

[0307] Each detection event within a given simulation model is assigneda unique name in a manner described below. During model build,instrumentation load tool 464 (FIG. 4D) generates a data structure inthe form of a table within the simulation model that uniquely names allthe detection events within a given simulation model and thecorresponding instrumentation entity output signals. This table will bereferred to hereinafter as the detection event translation table.

[0308] An API function GETEVENT( ) is provided within software simulator1410 for accessing model detection events. API function GETEVENTreferences a detection event translation table 1470 to locate signals1466 and 1468 in response to a call 1472 by RTX to obtain the value ofmodel event event_x. RTX 1405 obtains the value of model event event_xwithout delivering a number of GETFAC API calls, and furthermore,without the need to process the signal values associated with the modelevent. The RTX code is thus insulated from potential changes to thesignals and signal sequence values defining model event event_x. Anychanges to the detailed definition of model event event_x are reflectedwithin instrumentation entity 1460 and no changes to the RTX arenecessary.

[0309] With reference to FIG. 14C, there is illustrated an exemplary HDLsource code file 1480 that describes instrumentation entity 1460 inaccordance with a preferred embodiment of the present invention. Asshown in FIG. 4C, exemplary file 1480 consists of a number of entitydescriptor comments 1491 and an architecture section 1492 comprisinginstrumentation logic 1464.

[0310] Within HDL file 1480, a set of input port map comments 1493 serveto generate connections 1462 of FIG. 14B. An additional comment section,detection declarations 1494 is incorporated within the entity descriptorcomment syntax that allows for declaring detection events. A detectiondeclaration comment 1495 serves to generate and uniquely name detectionevent event_x. Moreover, detection declaration comment 1495 associatessignals 1466 and 1468 of FIG. 14B with event_x. Detection eventdeclarations, such as detection event declaration 1495 are of the form:

[0311] ——!!<name>: event_value_port [ctrl_signal];

[0312] where name is a name associated with the specific detection event(event_x in FIG. 14C), event_value_port is the output port providing thevalue for the detection event, and ctrl_signal is an optional single bitoutput port that flags an occurrence of the model event.

[0313] Each detection event is uniquely named in accordance with thename field within the output declaration comment in a manner analogousto that described earlier for count events. Such detection event names,together with the corresponding instrumentation entity output ports, areinserted into the detection event translation table data structure thatis placed within the model by instrumentation load tool 464. APIfunction GETEVENT receives the extended event identifier associated witha given event as an input and returns the model event value and, ifapplicable, an indication of whether the event occurred in the currentcycle.

[0314] While FIG. 14C illustrates only those constructs necessary forimplementing a detection event, the principles set forth herein place nolimitation on the generation of count, fail, and harvest events orsignal overrides within the same instrumentation entity as a detectionevent. Moreover, multiple detection events may be incorporated withinthe same instrumentation entity.

[0315] Within the spirit and scope of the present invention, detectionevents may be created within random instrumentation comments in a mannerlargely similar to that described with reference to signal overrides.Detection events can also be combined, in a manner similar to that shownearlier, as part of a hierarchical event.

[0316] Finally, it should be noted that the present invention may bepracticed in conjunction with a hardware simulator. As for softwaresimulators, hardware simulators are controlled by an RTX program. Toadapt the principles set forth herein to a hardware simulatorenvironment, the hardware simulator provides a GETEVENT API function andaccept models containing a detection event translation table.

[0317] By utilizing random instrumentation comments, a design engineercan efficiently create representations of model events accessible toRTX. Such representations need not change even if the detaileddefinition of the model event changes. Such stability reduces thecomplexity and burden of maintaining RTX and lowers the amount ofcommunication required between design and verification engineers.

[0318] In order to provide for the control and monitoring ofinstrumentation events within simulation models executing on a batchsimulation farm, one or more general-purpose computers, hereinafterreferred to as “instrumentation servers”, are added to batch simulationfarms. An instrumentation server acts as a centralized repository forinformation used to control instrumentation events and for data gatheredfrom instrumentation events during simulation runs. The exact nature andfunction of the control information and of the gathered data varies withthe type of event (i.e. fail events vs. count events), as will bedescribed below.

[0319] In order to allow for effective management of instrumentationevents, a set of “eventlist” files (described with reference to FIGS.10A-D) contain information about the exact number and content of theinstrumentation events in a given model. The eventlist files are createdat model build time by instrumentation load tool 464. These files, oneper class of events (fail, count, harvest, etc.), list the particularevents in the given simulation model. Each simulation model has a uniqueset of corresponding eventlist files that are created at model buildtime.

[0320] When instrumentation events are created at model build time, theyare constructed in a specific order, and a unique index is assignedwithin the eventlist file to each instrumentation event for a givenevent class. Accesses to instrumentation events by API routines make useof these index values. Furthermore, when an API routine communicatesaggregate data with respect to all events within a given event class tothe instrumentation server, this aggregate data is sequentially orderedaccording to these index values.

[0321] Each eventlist file contains a list of the instrumentation eventsfor a given event class within the model. These events are named inaccordance with the naming convention data structures described above inconjunction with FIGS. 10A-C, which provides unique names for each ofthe instrumentation events. Referring back to FIG. 10A in conjunctionwith FIG. 15, there is shown an eventlist file 1660 for the count eventsof simulation model 1000 of FIG. 10A.

[0322] Eventlist file 1660 contains multiple count event class entries1663. Each of count event class entries 1663 includes a unique,sequential index value 1661, and an extended event identifier 1662. Eachof indices 1661 corresponds to the index of a particular event (in thiscase count event COUNT1) assigned at model build time. Extended eventidentifiers 1662 provide an event name associated with each individualevent index. Eventlist file 1660 thus provides a mapping between theinstrumentation event names and the instrumentation event indices aswell as providing an ordering convention for aggregate data for a classof instrumentation events. Eventlist files, such as evenlist file 1660,are used by the instrumentation server to aid in the control andmonitoring of instrumentation events in simulation models.

[0323] With reference now to FIG. 16B, there is illustrated a batchsimulation farm 1601 in which a preferred embodiment of the presentinvention may be implemented. Batch simulation farm 1601 consists ofgeographically distant simulation farm nodes 1680 a-d. Within thesenodes, general-purpose computers 1600 a-n are interconnected via localarea networks (LANs) 1610 a-d. LANs 1610 a-d are further connected bymeans of a wide-area network (WAN) 1690, which provides communicationamong multiple simulation farm nodes 1680 a-d. Those skilled in the artwill recognize that many possible network topologies are possible for abatch simulation farm.

[0324] One such general-purpose computer 1607, together with a set ofdisk storage devices 1609 serve as a shared file system, which isaccessible to all general-purpose computers within simulation farm nodes1680 a-d. Exemplary batch simulation farm 1601 has been shown with oneshared file system server in a particular geographic node. Those skilledin the art will recognize that it is possible for the shared file systemto be implemented as multiple general-purpose computers and disk devicesacross the different geographic nodes in the batch simulation farm.Further, it is possible for each distinct geographic node to have aunique shared file system. Such unique file systems are usuallyaccessible to all nodes, but are most advantageously accessed within thelocal network node wherein the file system resides.

[0325] Within simulation farm node 1680 a, a particular general-purposecomputer serves as an instrumentation server 1699. Although a singleinstrumentation server is described with reference to the batchsimulation farm environment shown in the figures, those skilled in theart will understand the extensions necessary to distribute thefunctionality of the instrumentation server across several physicalgeneral-purpose computers.

[0326] General-purpose computers 1600 a-n within simulation farm nodes1680 a-d utilize software or hardware simulators to perform simulationtests on various digital design simulation models. In addition, certaindesignated general-purpose computers 1600 within batch simulation farm1601 serve specific roles in the process of controlling and executingsimulation tests as described below. Many of general-purpose computers1600 may also be user machines that execute simulation tests as asecondary background function.

[0327] At any given time, a number of distinct versions of a simulationmodel for a given digital logic design may be undergoing simulationwithin batch simulation farm 1601. In addition, a number of differentdigital designs, each with their respective differing model versions,may be undergoing simulation. In such circumstances, each differentmodel is typically given a name, hereinafter referred to as the “modelname”, which uniquely identifies both the particular digital design andthe particular version of the simulation model for the given digitaldesign.

[0328] One or more general-purpose computers 1600, hereinafter referredto as “model servers”, are utilized to store the valid versions ofsimulation models currently available for execution within batchsimulation farm 1601. Before simulation jobs can be executed withinbatch simulation farm 1601 with respect to a particular model, thatmodel must be built, and a copy of the model placed on the modelservers. In addition, the eventlist files for the model must be placedon instrumentation server 1699 to allow for the control and monitoringof the instrumentation events.

[0329] Within batch simulation farm 1601, one or more of general-purposecomputers 1600 a-n, referred to hereinafter as “testcase generators”,are typically utilized to create simulation testcases for the variousmodels under simulation. The testcase generators are responsible forgenerating tests to be executed and further packaging these tests intosimulation jobs. A simulation job is an entity containing the simulationtest and any controlling information and/or programs (such as RTX) thatare necessary to execute the simulation test within batch simulationfarm 1601.

[0330] Simulation jobs are passed from the testcase generators to one ormore of general-purpose computers 1600 a-n that are utilized as batchcontrollers, within batch simulation farm 1601. These batch controllersare responsible for dispatching simulation jobs to a general-purposecomputer utilized as a simulation platform, herein after referred to asa “simulation client”.

[0331] Once a simulation job arrives at a simulation client, thesimulation client communicates with the model servers to obtain a copyof the simulation model corresponding to the particular simulation job.The model can be transferred to the simulation client by a number ofmeans well known to those skilled in the art including, among others, ashared file system, File Transfer Protocol (FTP), or a custom filetransport mechanism utilizing network communication protocols.

[0332] In addition, the simulation client communicates withinstrumentation server 1699, the shared file system comprisinggeneral-purpose computer 1607 and disk storage devices 1609, or somecombination thereof, in order to obtain the control information for theinstrumentation events within the model. This control information isstored on a per model basis by model name on instrumentation server1699. The exact contents and nature of the communication between thesimulation client and instrumentation server 1699 varies with the typeof events within the model as explained in further detail below. Theinstrumentation event control information is used by API routines calledby RTX to control the behavior of the instrumentation events within thesimulation model.

[0333] The simulation model is then loaded either into memory 44 or thehardware simulator within the simulation client. Model processingcontrol is then passed to RTX for the execution of the simulationtestcase. RTX executes the testcase until the successful completion ofthe test or an error condition (test fail) occurs.

[0334] Within batch simulation farm 1601, one or more of general-purposecomputers 1600 a-n, hereinafter referred to as “statistics servers”, areutilized to store general statistics, such as cycles completed, numberof passing tests executed, etc. concerning the execution of simulationjobs within batch simulation farm 1601. Likewise, one or more ofgeneral-purpose computers 1600 a-n, hereinafter referred to as “failedtestcase servers”, are utilized to store simulation tests that havefailed in order to facilitate the re-execution and debugging of thesetestcases.

[0335] At the conclusion of the execution of a testcase, whether due tosuccessful execution or a failure, RTX communicates with the statisticsserver to record general statistics about the execution of thesimulation job. Such communication can be accomplished in a number ofways well known to those skilled in the art including a shared filesystem, a direct network connection between RTX and the statisticsserver, a file transport mechanism, and others.

[0336] At the conclusion of a testcase, RTX also communicates theaggregate information concerning instrumentation events toinstrumentation server 1699. This information is stored oninstrumentation server 1699 for future analysis and in some cases isutilized to control instrumentation events in future simulation testcaseruns for a given model. The exact nature of this communication variesfor the different event classes as explained in further detail below.

[0337] If a testcase concludes due to a failure, RTX communicates withthe failed testcase server to save those elements of the simulation jobrequired to allow for the reproduction of the failed simulationtestcase. The failed testcase server serves as a repository of failedtests that may be retrieved and re-executed in a foreground manner toallow for detailed investigation and problem resolution.

[0338] It is important to note that different simulation modelstypically require differing forms of testcases. What constitutes atestcase varies, often dramatically, between different simulationmodels. This is due to the varied techniques utilized in the present artfor simulation of digital systems. In such circumstances, the failedtestcase servers provide mechanisms capable of storing each of thevarious different forms of testcases.

[0339] In response to RTX communicating the general statistics for asimulation job to the statistics servers, communicating the aggregatestatistics for the instrumentation events to instrumentation server1699, and arranging for the storage of any failed simulation testcaseson the failed testcase servers, RTX terminates and the simulation clientis released. The batch controllers can then dispatch a new simulationjob to the simulation client for execution. Those skilled in the artwill recognize that many potential variations in the operation of abatch simulation farm are possible.

[0340] With reference to the flowchart of FIG. 16C in conjunction withFIG. 15, there is depicted a progression of events from the creation ofa specific simulation model to the removal of that model from batchsimulation farm 1601 and instrumentation server 1699. The process beginsat step 1621, which depicts the creation of the given simulation model.The simulation model is created in accordance with model build processesdescribed hereinbefore.

[0341] Proceeding to step 1622, the model is placed on the model serverto be available for simulation jobs executed within batch simulationfarm 1601. Next, as illustrated at step 1655, the model eventlist filesare placed on instrumentation server 1699. Once the eventlist files fora given model are placed on instrumentation server 1699, instrumentationserver 1699 begins controlling instrumentation events and gatheringinstrumentation event data for the given model. Placing the eventlistfiles on instrumentation server 1699 will be referred to hereinafter as“commissioning” a model.

[0342] The process continues as depicted at step 1623, with adetermination of whether all the desired testing for the given model hasbeen completed. If, as illustrated at step 1625, all testing for thegiven model is not complete, a new testcase is generated by the testcasegenerators. Subsequent to generation of a new testcase, a batchcontroller dispatches the resultant simulation job to a simulationclient for execution as shown at step 1626. The simulation job is thenexecuted on the simulation client as depicted at step 1627. Finally, theprocess returns to step 1623 to repeat until model testing for the givenmodel is complete and the model is removed from the batch simulationfarm as illustrated at step 1624.

[0343] Those skilled in the art will recognize that it is possible forseveral concurrent simulation jobs for the same model to be executingcontemporaneously within batch simulation farm 1601. That is to say,steps 1625-1627 may be executed with respect to the same model a numberof times concurrently by batch controllers within batch simulation farm1601. The given simulation model is not removed from batch simulationfarm 1601 until all outstanding jobs, potentially executingconcurrently, for the given simulation model have completed execution.Referring to step 1624, when all testing for the model has beencompleted, the model is removed from the model servers and thereforefrom batch simulation farm 1601.

[0344] It is often necessary to access particular elements of theinstrumentation data for a particular model even after the model hasbeen removed from batch simulation farm 1601. Process step 1628 depictsa determination of whether there still exists a need for access to theinstrumentation data stored within instrumentation server 1699 for thegiven model. In response to a determination that all necessary access toinstrumentation data for the model has been completed, the processcontinues as shown at step 1629, with the eventlist files, controlinformation, and instrumentation data files for the given model allbeing removed from instrumentation server 1699, thereby removing themodel in its entirety from instrumentation server 1699.

[0345] With reference to the flowchart of FIG. 16D, the steps involvedin simulation job execution step 1627 of FIG. 16C are depicted ingreater detail. The process of executing a simulation job on asimulation client begins with step 1631, which depicts the simulationclient obtaining a copy of the model corresponding to the givensimulation job provided by the model servers. As illustrated at step1638, the simulation client communicates with instrumentation server1699 to obtain and process control information for the instrumentationevents within the simulation model. Proceeding to step 1632, thesimulation model is loaded into a hardware simulator or memory 44 of thesimulation client.

[0346] The process then moves to step 1633, which depicts the executionof the simulation test under RTX control. Once the simulation test iscomplete, and as illustrated at step 1634, RTX delivers the aggregatestatistical results data obtained from the simulation job to thestatistics server wherein the data are logged. Next, as depicted at step1635, a determination is made of whether or not the testcase failed. Ifthe testcase has failed, and as shown at step 1636, the RTX communicatesthe failed testcase to the failed testcase servers. If it is determinedat step 1635 that the testcase did not fail, the process continues atstep 1637, which depicts RTX delivering various aggregateinstrumentation event data to the instrumentation server 1699 as will bedescribed below. The process concludes with step 1639, illustrating thesimulation client being released to perform other simulation jobs.

[0347] In an environment such as batch simulation farm 1601, whichcontains a potentially large number of different versions of activesimulation models, it is important to ensure the correctness of theaggregate instrumentation data communicated from the simulation clientsto the instrumentation server for the various models. Each simulationmodel contains a unique set of instrumentation events that are ordered,per class of events, in a unique fashion. The nature and identity ofthese sets of events are delivered to instrumentation server 1699 whenthe eventlist files for each model are commissioned onto instrumentationserver 1699.

[0348] Aggregate data about a group of instrumentation events deliveredfrom a simulation client are identified by the model name withininstrumentation server 1699. Once a particular simulation model iscommissioned (i.e. the eventlist files for the model has been placed oninstrumentation server 1699), it may be useful to enable instrumentationserver 1699 to determine if the aggregate instrumentation data receivedunder a given model name correctly corresponds to the instrumentationdata for the model named in accordance with the model name originallycommissioned on instrumentation server 1699.

[0349] Simulation clients 1600 a-n may also be utilized to simulate amodel directly in a foreground mode without using the batch simulationfarm. Such a foreground simulation does not require the placement of amodel on the model servers or the placement of the eventlist files onthe instrumentation server. Such independent model processing is onepossibility necessitating a method for ensuring consistency of eventlistmodel data within instrumentation server 1699. To provide a means touniquely correlate aggregate instrumentation data to a specificsimulation model, instrumentation load tool 464 places a number ofso-called “digital signatures” within each simulation model. Thesesignatures are computed for each instrumentation event class and aresubsequently utilized by a cyclic-redundancy-check (CRC) check functionto ensure an exact correspondence between models commissioned withininstrumentation server 1699 and subsequently arriving model eventinformation. As explained in further detail with reference to FIGS.17A-C, a digital signature is generated that uniquely corresponds to theeventlist contents for each event class. The digital signature iscomputed from the eventlist data for a given set of events within aspecific simulation model. Any alterations, additions, or deletions toan eventlist file will result in a differing digital signature.

[0350] The use of CRC values is well known to those skilled in the artand they are used in a variety of other circumstances to detectcorruption of data during transmission or storage. As implementedherein, however, the CRC “digital signature” serves not to detect errorsin the physical transmission or storage of data, but rather as a uniquesignature for the contents of an eventlist file for a simulation model.For example, if a model with a different set of instrumentation eventsis created, but given the same name as an earlier model, the contents ofthe eventlists are changed and therefore the value of the CRC digitalsignatures will differ from those of the original model.

[0351] Referring to FIG. 17A, there is illustrated a block diagramdepicting data content within main memory 44 (FIG. 2), including asimulation client 1701, during a simulation run of a simulation model1700 in accordance with a preferred embodiment of the present invention.Within simulation model 1700, digital signatures 1710 a-n correspond toa CRC value calculated by instrumentation load tool 464 for the variouseventlists describing all instrumentation events contained in simulationmodel 1700.

[0352] At the conclusion of a simulation run, an RTX 1702 communicatesaggregate instrumentation event data to instrumentation server 1699, asdepicted in step 1637 of FIG. 16D. To communicate the aggregate eventinstrumentation data, RTX 1702 calls API entry point 1740 within asimulator 1735. API entry point routine 1740 collects theinstrumentation event data into an “aggregate data packet” (depicted inFIG. 17B), which is delivered to instrumentation server 1699 through anetwork interface 1720. Distinct API entry points are provided for eachclass of instrumentation events that must communicate aggregate datawith instrumentation server 1699.

[0353] With reference to FIG. 17B, an aggregate data packet 1750, suchas that delivered by API entry point routine 1740 to instrumentationserver 1699, is depicted. Aggregate data packet 1750 contains a modelname field 1751, a CRC digital signature value 1752, and a data field1753. Model name field 1751 consists of the name of simulation model1700. CRC digital signature value 1752 contains the digital signaturevalue for the class of events communicated in aggregate data packet1750. Data field 1753 contains the aggregate instrumentation event datafor model 1700. The nature and contents of this data varies for eachclass of instrumentation events.

[0354] Referring to FIG. 17C, there is shown a process by which modelevent data for aggregate data packets received by instrumentation server1699 is validated in accordance with a preferred embodiment of thepresent invention. The process begins at step 1770 with instrumentationserver 1699 receiving an eventlist for a specific model during modelcommissioning. Next, as illustrated at step 1772, instrumentation server1699 computes and stores a CRC digital signature uniquely characterizedby the contents of the eventlist. Instrumentation server 1699 employsthe same CRC computation function as that utilized by instrumentationload tool 464 to generate the CRC digital signature value such as CRCvalue 1752.

[0355] Proceeding to step 1774, instrumentation server 1699 receives anaggregate data packet structured as depicted in FIG. 17B from simulationclient 1701. It should be noted that the CRC value contained with theaggregate packet received at step 1174 was previously computed andstored by instrumentation load tool 464. The process continues asdepicted at step 1776, with a determination of whether or not theaggregate data packet corresponds to a model and event class that haspreviously been commissioned with instrumentation server 1699. If not,and as illustrated at step 1782, the packet is discarded. If, however,the aggregate data packet corresponds to a class of event for a modelname commissioned on instrumentation server 1699, the process proceedsto step 1778.

[0356] Step 1778 depicts a determination of whether or not the CRCdigital signature contained within aggregate data packet 1750 matchesthe CRC value computed and stored at step 1772. If the CRC check valuesmatch, and as illustrated at step 1780, the packet is processed. Theexact nature of the processing varies with the type of packet asexplained in further detail below. If the CRC check values do not match,the packet is discarded as depicted at step 1782. Following eitherpacket processing (step 1780) or packet discard (step 1782), the processreturns to step 1774 at which a next arriving aggregate data packet isreceived.

[0357] By calculating and storing a CRC digital signature value for eacheventlist received when models are commissioned, instrumentation server1699 can verify that each aggregate data packet received for a givenmodel name matches the data contents of the model having that name whenoriginally commissioned on the instrumentation server. In this manner,instrumentation server 1699 can ensure that the data receivedcorresponds to the model (as defined by the model name) as that modelwas defined at the time it was commissioned.

[0358] In the context of a batch simulation farm, it is advantageous toprovide a means to centrally disable instrumentation events without theneed to recompile or redistribute simulation models. Such a mechanism isparticularly useful for centrally disabling a faulty or undesired failinstrumentation event within a simulation model that may be simulatedwithin any number of simulation clients within the batch simulationfarm.

[0359] Faulty fail instrumentation events may result in a large numberof testcases being erroneously reported as failing and subsequentlybeing erroneously stored for analysis. Although the followingdescription of centralized instrumentation event disablement isexplained only for fail events, one skilled in the art will appreciatethat similar techniques could be applied to instrumentation event typesother than fail events.

[0360]FIG. 18A illustrates contents of memory 44 within simulationclient 1701 during execution of a simulation job. Prior to execution ofthe simulation job, RTX 1702, as part of step 1638 of FIG. 16D, callsAPI entry point 1800, disable_events( ). API entry point 1800 is aroutine that communicates with instrumentation server 1699 and/or sharedfile system 1609 to obtain a list of events to be disabled, hereafterreferred to as a “fail disable list”. Separate, initially empty faildisable lists are stored for each active model within batch simulationfarm 1601.

[0361] Once the fail disable list is obtained by RTX 1702, API entrypoint 1800 further calls API entry point 1802, set_fail_Mask( ), todisable the specific failure events listed in the retrieved fail disablelist. To disable, or mask, the fail events specified in the fail disablelist, API entry point 1802 sets appropriate fail mask registers 507 a-nas described with reference to FIG. 5A.

[0362] API entry point 1800 obtains the fail disable list from one oftwo sources. The first source is a master file 1805 stored on disk ininstrumentation server 1699. The second possible source is from anauxiliary file 1807 stored in association with general purpose comptuter1607 in shared file system 1609. Master file 1805 serves as the primarycopy of the fail disable list. At regular intervals, instrumentationserver 1699 copies the contents of master file 1805 into auxiliary file1807, which serves as a backup copy of the fail disable list.

[0363] The two-file system depicted in FIG. 18A is used to maintain thefail disable list in a flexible and robust manner within geographicallydistributed simulation farm 1601. Simulation clients that obtain thefail disable list from shared file system 1609 can potentially receive a“stale” copy of the disable fail list. In practice, this may not pose aproblem since master file 1805 is copied onto auxiliary file 1807 on aregular interval, and any discrepancies between master file 1805 andauxiliary file 1807 are quickly reconciled.

[0364] In one implementation, simulation clients within the same localarea network as instrumentation server 1699 are configured to primarilyobtain the fail disable list by direct communication withinstrumentation server 1699. In response to a communication failure withinstrumentation server 1699, the simulation clients would attempt toobtain the fail disable list from shared file system 1609, which mayreside within or outside the requestor's local area.

[0365] A direct network connection with a geographically remoteinstrumentation server can potentially be more error prone and havelower performance than a local connection to a geographically localshared file system. Therefore, simulation clients that aregeographically remote (i.e. not in the same local area network) withrespect to instrumentation server 1699, may be configured to initiallyattempt to obtain the disable fail list from a local shared file system.In alternate implementations, simulation clients may be configured toobtain the fail disable list from shared file system 1609 in order toreduce network traffic to instrumentation server 1699. By having twoseparate sources for the fail disable list, the simulation clients canbe configured to balance the traffic between instrumentation server 1699and shared file systems 1609.

[0366] Referring to the flowchart of FIG. 18B, wherein is depicted theprocesses performed with respect to API entry point 1800 in greaterdetail. As shown in FIG. 18B, the process begins with a fail listrequest by RTX 1702 as depicted at step 1829. In response to the RTXrequest, and as illustrated at step 1830, a determination is made ofwhether or not an attempt should be made to access instrumentationserver 1699 to obtain the disable failure list. If a determination ismade not to attempt to access the fail disable list from instrumentationserver 1699, the process continues at step 1836, with a furtherdetermination of whether or not to access shared file system 1609.Otherwise, as illustrated at step 1832, simulation client 1701 attemptsto obtain the fail disable list from instrumentation server 1699.

[0367] Proceeding to step 1834, if the attempt to access instrumentationserver 1699 was successful, the fail events specified in the faildisable list are disabled via API entry points 1800 and 1802 asillustrated at step 1844. In the case of an unsuccessful attempt toaccess instrumentation server 1699, and as depicted at step 1836, afurther determination is made of whether to attempt to access sharedfile system 1609 in an alternative attempt to obtain the fail disablelist. As depicted at steps 1838, 1840, and 1844, the fail eventsspecified in the fail disable list are disabled (i.e. API entry point1800 calls API entry point 1802 to disable the designated failureevents) in response to simulation client 1701 successfully accessingshared file system 1609. The failure event disablement process concludesstep 1846, depicting API entry point returning a successful disablementindication to RTX 1702. If the attempt to access shared file system 1609is unsuccessful, the process concludes with step 1842, illustrating APIcall 1800 returning an indication to RTX 1702 that the attempt to maskthe failure events failed.

[0368] To initiate disablement of one or more fail events for a givensimulation model, a user adds an entry to a fail disable list associatedwith the simulation model within master file 1805. Subsequently,simulation clients utilizing the fail disable list within master file1805 will disable the fail event(s) for the particular simulation modelin accordance with the process illustrated in FIG. 18B. At apre-determined interval, the fail disable list within master file 1805is delivered to replace the failure disable list within auxiliary file1807, and the currently active simulation clients will disable thefailure event(s) specified in the updated list for the specified model.

[0369] Within a fail disable list file, fail events are specified byentries corresponding in structure to the event identifiers for failureevents with possible wildcard extensions in the various eventnamefields. Such wildcard extensions permit, for example, the automaticdisablement of all the replicated instances of a given failure eventwithout having to explicitly list all the instances of the failureevent. However, by utilizing entries without wildcards within thefailure disable file, the failure disable list provides the ability toselectively disable specific individual failure event instances as well.

[0370]FIGS. 18A and 18B illustrate a user-initiated mechanism forcentrally disabling fail events within a batch simulation farmenvironment. Typically, batch simulation farms run continuously andcannot be directly monitored at all times. As will be explained withreference to FIGS. 19A and 19B, the present invention provides anautonomous means of disabling failure events that does not requireactive user intervention.

[0371] With reference to FIG. 19A, there is depicted contents of memory44 within simulation client 1701 at the conclusion of a simulation jobin accordance with a preferred embodiment of the present invention.Simulation model 1700 contains fail events identified within fail eventflag registers 500 a-n as described hereinbefore in conjunction withFIG. 5A. Signal 511, driven by logical OR gate 512, indicates theoccurrence of a failure event during a simulation job.

[0372] As part of step 1637 of FIG. 16D, RTX 1702 calls an API entrypoint 1900, report_fails( ). API entry point 1900 first examines signal511 to determine if any of the fail events specified within flagregisters 500 a-n have occurred during a simulation run. If none of thespecified failure events have occurred, API entry point 1900 terminatesfurther action.

[0373] However, if one or more of the specified failure events haveoccurred during the simulation run, API entry point 1900 generates anddelivers a corresponding aggregate data packet for the occurring failureevents via network interface 1720 to instrumentation server 1699. Thecontents of registers 500 a-n are contained, for example, in the datafield of the aggregate data packet delivered to instrumentation server1699. In this manner, instrumentation server 1699 receives informationfor every failure event that occurs within batch simulation farm 1601.In accordance with the depicted embodiment, instrumentation server 1699maintains a set of counters 1901, one set per commissioned model, tomonitor the rate of occurrence for individual failure events.

[0374] After verifying the correctness of the fail event aggregate datapacket, in accordance with the process described above with reference toFIG. 17C, instrumentation server 1699 increments counters 1901 to recordthe occurrence of the failure events in model 1700. Contemporaneouslywith processing aggregate failure event data packets, instrumentationserver 1699 decrements counters 1901 at a regular interval. In thismanner, counters 1901 indicate the number of times a failure event hasoccurred within a given time interval rather than the total number oftimes a failure event has occurred. The interval at which counters 1901are decremented is a predetermined value that can be adjusted.

[0375] Within counters 1901, only one counter is provided per specificfail event without regard to the differing instances of the specificfail event. That is to say, failure events are considered in anon-hierarchical sense as described above. Occurrences of fail eventsfrom all instances of a given fail event are added into a singlecounter. Each of counters 1901 therefore represents the rate at which agiven failure event occurs, irrespective of which instance or instancesof the failure event occur. In practice, it is preferable to considerfailure events in a non-hierarchical sense, since, in general, thesignificance of a given failure event does not depend on where insimulation model 1700 the failure occurs.

[0376] Referring to FIG. 19B, there is depicted a flow diagram of aprocess by which instrumentation server 1699 processes fail eventaggregate data packets in accordance with a preferred embodiment of thepresent invention. The process begins at step 1910 and proceeds to step1912, which depicts instrumentation server 1699 receiving a fail eventaggregate data packet. The process continues with step 1914,illustrating a determination of whether or not the aggregate data packetcorresponds to a model commissioned within instrumentation server 1699in accordance with the digital signature verification method explainedwith reference to FIGS. 17A-C. If the aggregate data packet cannot beverified, it is discarded as illustrated at step 1916.

[0377] Otherwise, if the aggregate data packet has been verified ascorresponding to a commissioned model, counters 1901 are incremented inaccordance with packet data content within instrumentation server 1699as depicted at step 1918. The fail threshold process of the depictedembodiment is based on rates of occurrences of fail events rather than acumulative evaluation. To this end, and as depicted at steps 1925 and1927, all of counters 1901 are decremented at a predetermined timeinterval during processing of received aggregate fail event packets.

[0378] Following counter incrementation, and as illustrated at step1920, a determination is made of whether or not any of counters 1901 hasexceeded a predetermined threshold. Any counter having exceeded thisthreshold indicates that a given fail event is occurring at toofrequently (i.e. at an excessive rate). Responsive to a determinationthat any of counters 1901 has exceeded its threshold, the processcontinues as illustrated at step 1922, with instrumentation server 1699adding an entry for the excessively occurring fail event into the faildisable list within master file 1805. This entry disables all instancesof the problematic failure event within simulation model 1700, and withrespect to the disabled failure event, the process terminates as shownat step 1924.

[0379] If, as determined at step 1920, no counters have exceeded thethreshold, the fail threshold process terminates as shown at step 1924with respect to the packet received at step 1912. Instrumentation server1699 repeats the steps depicted in FIG. 19B for each fail eventaggregate data packet received from API entry point 1900, report_fails().

[0380] The process illustrated in FIG. 19B provides a means by whichinstrumentation server 1699 monitors the occurrence of failure eventswithin simulation models executed on batch simulation farm 1601. When agiven failure event occurs faster than a certain threshold,instrumentation server 1699 automatically disables the failure event.This process occurs without the need for user intervention.

[0381] In an environment such as batch simulation farm 1601, it iscommon for a given design entity to be utilized in a number of differentmodels of varying complexity and size. A given design entity may appearin different models ranging in complexity from a model containing asubset of the integrated circuit in which the entity resides to a modelcontaining an entire system with potentially multiple instances of thephysical chip in which the design entity resides. Furthermore, there maybe several versions of each of these different models active withinbatch simulation farm 1601 at any given time.

[0382] In such an environment, it advantageous to provide a means thatallows an HDL circuit designer to access count event data for a givendesign entity without requiring specific knowledge of which activemodels in the batch simulation farm contain that design entity. Inpractice, designers are generally not aware of the specifics of themodels active in a batch simulation farm at any given time.

[0383] In addition, once a designer registers a request for count eventdata, it is useful if this request can be repeated without userintervention at specified intervals, and that the counter data bereturned automatically to the designer's workstation for on-goingevaluation. In the following description, a request (from an HDLdesigner, for example) for counter data submitted within a batchsimulation farm environment will be referred to as a “counter query”.Counter queries are delivered from one of general-purpose computers 1600to instrumentation server 1699 for storage and processing. In accordancewith the embodiments described herein, a separate list of counterqueries is maintained for each individual user.

[0384] With reference to FIG. 20A, there is depicted the contents ofmemory 44 at the conclusion of a simulation processing job performedwith respect to simulation model 1700 within simulation client 1701.Simulation model 1700 contains count event registers 421 a-421 n asdescribed hereinbefore with reference to FIG. 4B. Each of count eventregisters 421 a-421 n maintains a count representing the number of timesa particular instrumentation count event has occurred during thesimulation of simulation model 1700.

[0385] As part of step 1637 of FIG. 16D, RTX 1702 calls an API entrypoint rpt_counts( ) 2000. API entry point 2000 generates and delivers anaggregate data packet containing the results registered in count eventregisters 421 a-421 n to instrumentation server 1699 via network 1720.Upon receipt of the aggregate count event data packet, instrumentationserver 1699 confirms that the packet information corresponds to acommissioned simulation model utilizing a CRC digital signature asdescribed with reference to FIGS. 17A-17C. If the aggregate data packetcorresponds to a commissioned model, instrumentation server 1699 storesthe count data within the aggregate count event packet in a set of countdata storage files 2001 a-2001 n.

[0386]FIG. 20B depicts an exemplary aggregate count event packet 2010 inaccordance with a preferred embodiment of the present invention. Similarto aggregate data packet 1750 of FIG. 17B, aggregate count event packet2010, includes model name field 1751, CRC digital signature field 1752,and data field 1753. Within data field 1753, a cycle count field 2011contains a count value representing the number of cycles executed duringthe simulation run from which aggregate count event packet 2010 wasgenerated. A set of count value fields 2012 a-n contain the count valuesfor each of the count events instantiated within simulation model 1700in the order set forth by the count eventlist file created at modelbuild time.

[0387] Within instrumentation server 1699 depicted in FIG. 20A, thecount event data contained in the count value fields for one or moreaggregate count event packets is stored in count data storage files 2001a-n. Each of count data storage files 2001 a-n therefore contains allrecorded counts for a given predetermined time interval (typically aday) for a specified simulation model. When instrumentation server 1699receives and confirms the commissioned status of aggregate count eventpacket 2010, one of count data storage files 2001 a-n is either createdor updated as necessary to store the contents of the received aggregatecount data packet 2010.

[0388] Referring to FIG. 20C there is illustrated the contents of anexemplary count data storage file 2001 among count data storage files2001 a-n. Within count data storage file 2001, a cumulative cycle countfield 2020 represents the cumulative number of cycles simulated duringevery simulation run for which count data is added into count datastorage file 2001 over the aforementioned predetermined time interval. Aset of cumulative count fields 2021 a-2021 n contain the cumulativenumber of occurrences of each corresponding count event included withincount value fields 2012 a-2012 n for each aggregate count event packetreceived by instrumentation server 1699. In summary, when aggregatecount event packet 2010 is received and verified by instrumentationserver 1699, cycle count field 2011 is added to cumulative cycle countfield 2020 and likewise, count value fields 2012 a-2012 n are added tocorresponding cumulative count value fields 2021 a-2021 n. Count datastorage file 2001 therefore contains a cumulative total of the number ofcycles executed on the given simulation model and the number of timeseach count event has occurred over a pre-designated time interval whichin the depicted embodiment is a day.

[0389]FIG. 20D illustrates a directory structure implemented withininstrumentation server 1699 for storing count data storage files 2001a-2001 n. All count data is stored under a counter storage directory2030 on a disk storage unit 2007 within instrumentation server 1699. Asdepicted in FIG. 20D, counter storage directory 2030 is further dividedinto a two-tier subdirectory structure.

[0390] A first tier of subdirectories 2031 is utilized to associatecount data contained within a received aggregate count event packet witha particular time period (e.g. a specific date) in accordance with apre-designated time increment. In the embodiment depicted in FIG. 20D,this pre-designated time increment is a day (i.e. one 24-hour period).Each of subdirectories 2031 therefore contains the count data for allsimulation models received on a particular date. Each of subdirectories2031 is named in a fixed manner that is derived from the date on whichthe subdirectory was created. In association with each of subdirectories2031, a second tier of subdirectories 2032 is utilized to divide thecount data received by instrumentation server 1699 on a given day intodirectories indexed on a per-model basis. Therefore, each ofsubdirectories 2032 includes all count data collected for a particularsimulation model on a particular date. As shown in FIG. 20D, each ofsubdirectories 2032 is named in a fixed manner that is derived from themodel name of the given model.

[0391] As further illustrated in FIG. 20D, each of subdirectories 2032contains a corresponding one of count data storage files 2001 a-2001 n,containing the count data for a specified simulation model collected ona given date. The directory/subdirectory structure contained within diskstorage unit 2007 provides an efficient directory path for locatingcount event data for any given simulation model generated on aparticular date. For example, sorting count data first by day and thenby simulation model, simplifies removal of stale data frominstrumentation server 1699. Data for all active simulations models thatis obtained during an expired past time interval can be removed simplyby removing the appropriate subdirectory 2031 and its contents.

[0392] As a further improvement in processing counter queries, and inaccordance with an important feature of the present invention,instrumentation server 1699 generates and maintains a “count evententity translation table” that is indexed on a per-design-entity basissuch that all design entities contained within simulation models forwhich aggregate count event data has been received are listed, and arefurthermore associated with a list of all of the simulation models inwhich they are instantiated.

[0393] To allow for the construction of the entity translation tablewithin instrumentation server 1699, instrumentation load tool 464 isfurther augmented to produce an “entitylist file” as a part of the modelbuild process for each simulation model. The entity list file listsevery design entity within a given model in which instrumentation countevents are instantiated. Similar to the processing of event list files,entity list files are commissioned within instrumentation server 1699 asa part of step 1655 of FIG. 16C. Upon receiving an entity list file,instrumentation server 1699, adds the simulation model and design entityidentification contents of the entitylist file to the entity translationtable.

[0394]FIG. 20E illustrates a set of exemplary entity list files 2041a-2041 c that are incorporated within a count event entity translationtable 2044 in accordance with a preferred embodiment of the presentinvention. Each of the entity list files 2041 a-2041 c includes a listof design entity identifiers corresponding to every design entity withina respective simulation model (models X, Y, and Z) that includeinstantiated instrumentation count events.

[0395] Count event entity translation table 2044 maintains an index list2046 of the design entities (a, b, c, d, x, and y) having instantiatedinstrumentation count events and that are included within simulationmodels, including models X, Y, and Z, which have been commissioned oninstrumentation server 1699. A model list 2045 includes entriescorresponding to each design entity index within index list 2046denoting those simulation models that contain the design entitydesignated by the design entity index entry. The steps necessary togenerate count event entity translation table 2044 from entitylist files2041 a-2041 c are readily conceivable by one skilled in the art and aretherefore not described herein. When a simulation model isdecommissioned (i.e. records and data removed from instrumentationserver 1699), its corresponding entries within model list 2045 areremoved (potentially including removal of a design entity index when thelast model containing that design entity is removed) from count evententity translation table 2044. Instrumentation server 1699, utilizescount event entity translation table 2044 to ascertain which subset ofcount data storage files 2001 a-2001 n must be searched in response to auser count query for a given design entity or entities.

[0396] Elements within instrumentation server 1699 utilized for storing,managing, and executing user counter queries as depicted in FIG. 20F. Tocreate, delete, or modify counter queries, a user executes a counterquery interface program 2050 on general-purpose computer 1600. Counterquery interface program 2050 utilizes a graphical user interface (GUI)2051 on display 14 to allow the user to display, create, remove, or editcounter queries delivered to and stored within instrumentation server1699. A set of such counter queries 2053 a-2053 n are stored on diskstorage device 2007 within instrumentation server 1699. Each of counterqueries 2053 a-2053 n is stored in a separate file and containsinformation, described below with reference to FIG. 20H, denoting thetime at which the query is to be executed, the count data beingrequested, and the means by which to return the counter query data tothe user.

[0397] A counter query manager program 2052, residing within memory 44of instrumentation server 1699, executes counter queries 2053 a-2053 nand also returns the final output count data to the user. At regularintervals, counter query manager 2052 examines counter queries 2053a-2053 n to determine which of these queries should be run at any giveninterval. A determination to run a particular counter query is made byexamining information within the query itself which indicates a time atwhich the query is to executed within instrumentation server 1699.

[0398] Upon determining that a specific query among counter queries 2053a-2053 n is to be executed, counter query manager 2052 spawns aninstance of a counter query engine (CQE) program 2054 to process thequery. Counter query manager 2052 can spawn multiple instances ofcounter query engine program 2054 simultaneously if multiple counterqueries are to be executed over the same interval.

[0399] Counter query engine program 2054 utilizes information within thecounter query and count event entity translation table 2044 in order tosearch appropriate count data storage files 2001 a-2001 n for thecounter information specified in counter query 2053. At the end of thissearch, counter query program 2054 produces a query report hereinaftercalled a “basic counter output report”.

[0400] Referring to FIG. 20G, there is illustrated an exemplary basiccounter output report 2060. Within basic counter output report 2060, afirst integer field 2061 denotes the number of simulator cyclesaccumulated during simulation runs on simulation models containing thecount events for which a count has been requested in the counter query.The value stored within first integer field 2061 is derived by summingcount fields 2020 (FIG. 20C) for each of the counter data storage files2001 a-2001 n that contain the count events identified in the counterquery. Basic counter output report 2060 further contains a report list2062 including entries for each count event specified in the counterquery. Entries within list 2062 consist of count event identifier fields2063 and integer fields 2064 which denote the number of occurrences of acorresponding count event in accordance with information obtained fromcounter data storage files 2001 a-n.

[0401] Counter queries can specify either a hierarchical ornon-hierarchical query. In a hierarchical query, each replicatedinstance of any given count event is queried independently. As such, thebasic counter query report lists individual entries within list 2062 foreach replicated instance of a given count event. In such a circumstance,each of count event identifier fields 2063 corresponds to the extendedevent identifier described earlier in conjunction with FIG. 10B.Exemplary basic counter output report 2060 represents a hierarchicalreport for event “count1” within design entity “Z” within simulationmodel 1000 described earlier with reference to FIG. 10A.

[0402] Counter queries may alternatively be non-hierarchical, whereinall replicated instances of a given count event are grouped as a singleevent for reporting purposes. The counts for the individual instances ofthe count event are summed to form an overall count of the number oftimes the individual count event occurred in all instances.

[0403]FIG. 20G further illustrates a basic counter output report 2065that results from a non-hierarchical counter query. Non-hierarchicalbasic counter output report 2065 includes an event identifier 2066corresponding to the event identifier for the count events with theinstance identifier field removed. Since the various instances of agiven count event are combined into a single reported event, theinstance identifier has no meaning and is not included in thenon-hierarchical report.

[0404] Returning to FIG. 20F, after being produced by counter queryengine 2054, a set of basic counter output reports 2060 a-2060 n isstored within instrumentation server 1699 in a subdirectory 2055. Eachreport within subdirectory 2055 is named such that the date on which itwas created and the particular user counter query which created thereport can be determined. Basic counter output reports are stored byinstrumentation server 1699 to enable on-going analysis of longer termtrends of the counter data as described below.

[0405] Counter query engine 2054 may optionally be utilized forpost-processing of basic counter output report 2060 in order to producea graphical representation of the counter data such as a line plot,histogram or other such representation as a final counter query report2056. Each of counter queries 2053 a-2053 n specifies what, if any,additional post-processing to apply to basic counter report 2060 tocreate final counter query report 2056.

[0406] Final counter query report 2056 is returned to the user atgeneral-purpose 1600 over network interface 1720 by one of a number ofmeans that could include e-mail attachments, a specific file transferprotocol between instrumentation server 1699 and general-purposecomputer 1600, or copying the file to a shared file system among others.Each of counter queries 2053 a-2053 n specifies which mechanism(s) touse in returning the data to the user (it is possible to utilizemultiple methods to deliver multiple copies of final counter queryreport 2056).

[0407] With reference to FIG. 20H, there is depicted a representation ofthe query fields within an exemplary counter query 2053. Forillustrative purposes, the fields within counter query 2053 are dividedinto five field groups 2068, 2069, 2081, 2084, and 2087. Field group2068 provides general information regarding the identity of the queryand the identity of the user that initiated it. Within field group 2068,a field 2071 contains the name of the query. Query name field 2071provides a useful mechanism for referring to and specifying user counterqueries. Also within field group 2068, a username field 2072 containsthe username identity of the user that initiated the query and field2073 contains the name of the user machine from which the query wasinitiated. This information is utilized to allow counter query engine2054 to return finished counter report 2056 to the user.

[0408] Field group 2069 generally specifies the identity of the countevents to be queried in accordance with the remaining fields withincounter query 2053. Fields 2074, 2075, 2076, and 2077 correspond to theinstantiation, instrumentation entity, design entity, and eventnamefields in the count event extended identifiers as described hereinbeforewith reference to FIG. 10B. These fields, which can contain wildcardentries, are utilized to specify which instrumentation count eventswithin a simulation model are being requested. These fields are matchedby counter query engine 2054 against the count event eventlist filesstored on instrumentation server 1699 to locate the desired count eventswithin the simulation models searched during the query.

[0409] Field 2088 is an enable field (i.e. a flag) that determineswhether counter query 2053 is hierarchical or non-hierarchical. If theuser decides to perform a hierarchical query, instantiation identifierfield 2074 is used to locate count events during the query. If, however,the query is non-hierarchical as specified by field 2088, field 2074 isignored in searching for count events. The extended event identifier fordifferent instances of a given count event differ only in theirinstantiation identifier fields (field 1030 in FIG. 10B). By ignoringthe instantiation identifier field when searching for count events,differing instances of a given count event will appear as the same countevent and be merged in reporting.

[0410] Field group 2081 generally specifies the times at which to runthe query and how far back in time the query should span. In a preferredembodiment, instrumentation server 1699 allows user counter queries tospecify days of the week a query is to run and also to specify aparticular set of hours on those days the query is run. A day field 2078is a set of seven flags specifying which days of a week a query is torun, while an hours field 2079 is a set of 24 flags indicating the hourson those days a query is to run. Field group 2081 further includes alookback field 2080 that specifies the how far back in time a query isto examine data. When a query is run, it searches stored counter datafor the day it is run and for a number of preceding days. The number ofpreceding days to search is specified by field 2080.

[0411] Field group 2084 generally specifies what, if any, report postprocessing is to be performed and the report delivery mechanism. Areport post-process field 2082 specifies the report processing, if anyto apply to produce final counter report 2056. A report delivery field2083 specifies which delivery mechanism(s) to utilize in returning thefinished counter report to the user.

[0412] Field group 2087 generally specifies an optional restriction thatlimits the search of the counter data to specific models. This featureis useful to simulation teams that generally, in contrast to designers,prefer to query counter data with respect to a specific model or models,rather than querying count data at the design entity level. A simulationmodel search enable field 2086 holds a flag indicating whether or notcounter data searches are to be limited to specific models or not. Fieldgroup 2087 also includes a models field 2085, which may contain wildcardexpressions, specifying which models the search will be conducted withrespect to.

[0413] Referring to FIG. 20I in conjunction with FIG. 20H, there isshown a flowchart of the process by which counter query engine 2054produces basic counter output report 2060 from user query 2053. Theprocess begins at step 2090 corresponding to counter query manager 2052determining that query 2053 is ready to be run and spawning counterquery engine 2054 to process the query.

[0414] The process continues with step 2091 depicting a determination ofthe possible date subdirectories 2031 (FIG. 20D) that may need to besearched. To determine these directories, counter query engine 2054examines and compares the current date with the content of lookbackfield 2080 in counter query 2053. Using this information and standarddate processing techniques well known to those skilled in the art,counter query engine 2054 produces a list of directory namescorresponding to the current date and the number of days before thecurrent date specified by lookback field 2080. These directory namescorrespond to date subdirectories 2031 that must be searched inaccordance with counter query 2053.

[0415] Proceeding to step 2092, a determination is made of the modelsubdirectories among subdirectories 2032 a-2032 n within datedirectories 2031 that are to be searched. Counter query engine 2054creates a list of possible model directories by matching design entityfield 2076 against index list 2046 of entity list translation table 2044(FIG. 20E) and creating a directory list data structure (not depicted),removing duplications, of those models that correspond to matchingdesign entities. This list represents the set of active models oninstrumentation server 1699 that contain the design entity or entitiesspecified by design entity field 2076. In this manner, counter queryengine 2054 creates a list of simulation models within the directorylist data structure that are to be searched based solely on the designentity name without user intervention. The designer need not know whichactive models on instrumentation server 1699 contain the desired designentity or entities.

[0416] If simulation model search enable field 2086 of user query 2053is asserted (i.e. search only with respect to designated models), thelist of model subdirectories is further matched against models field2085. Those model subdirectories that do not match the content of modelfield 2085 are removed from the list generated in response to step 2092.In this manner, the counter query is further restricted to the specificset of model names specified by field 2085.

[0417] Next, as illustrated at step 2093, data structures necessary toprocess counter query 2053 are generated. The first data structurecreated is a list of directory paths relative to counter storagedirectory 2030 (FIG. 20F) that will be searched. These directory pathsare created by forming all possible combinations of the directory namesobtained in steps 2091 and 2092. It should be noted that some of thedirectory paths generated in this manner might not actually exist withincounter storage directory 2030. The process that generates the directorypaths generates all the paths that need to be searched whether or notcount event data has actually been received and stored for those modelsor days.

[0418] It is also possible that no directory paths match the criteriaspecified by query 2053. In such a circumstance, an empty directory listdata structure is generated. The second data structure generated is abasic count report data structure which is utilized to hold count eventsand count values found during processing. The basic count report datastructure is initially empty.

[0419] The process continues at step 2094 with a determination ofwhether or not the directory list data structure is empty. If thedirectory list data structure is currently empty, the process continuesat steps 2095-2097 where the results of the counter query are reportedto the user and stored. In such a circumstance, an empty report isgenerated because no counter data matches the criteria specified incounter query 2053.

[0420] If the directory list data structure is not empty, and asillustrated at step 2098, a determination is made of whether or not thecurrent directory in the directory list data structure exists withincounter storage directory 2030. If the directory does not exist, theprocess continues as depicted at step 2067 with the removal of thecurrent directory from the directory list data structure generated atstep 2092. Processing then returns to step 2094 wherein subsequentdirectories in the directory list data structure are processingaccordingly.

[0421] If, as determined at step 2098, the subdirectory exists amongsubdirectories 2032 a-2032 n, query processing continues as depicted atstep 2099 with a search of the simulation model for count eventscorresponding to those specified by counter query 2053. To locate countevents corresponding to counter query 2053, counter query engine 2054matches fields 2074, 2075, 2076, and 2077 against the extended eventidentifiers in the count eventlist file for the simulation model whosedata is stored in the identified one of subdirectories 2032 a-2032 n.The names and indices of matching count events are determined to allowfor subsequent retrieval of the counter value from a corresponding oneof counter data storage files 2001 a-2001 n.

[0422] Proceeding to step 2009, matching count events are used to updatethe basic count report data structure. If a matching count eventdiscovered in step 2099 is not present in the basic count datastructure, the count event is added to basic count data structure usingthe count value found in the corresponding one of count data storagefiles 2001 a-2001 n. Otherwise, the count value found in count datastorage file 2001 is added to the count event entry already present inthe basic count report data structure. This produces a cumulative totalfor the number of times the count event has occurred in the simulationmodels specified by counter query 2053.

[0423] The counter query process continues at steps 2067 and 2094wherein the directory is removed from the directory list data structureand a determination is made of whether or not any further subdirectoriesremain to be searched. If no unsearched subdirectories remain, theprocess continues at step 2095 which depicts storing the basic counterreport in directory 2055 on instrumentation server 1699 as previouslydescribed.

[0424] Next, as illustrated at step 2096, final counter report 2056 isgenerated utilizing the post-processing mechanism selected by field 2082of counter query 2053. The process is completed as depicted at step 2097with final counter report 2056 being delivered to the user by the meansselected by field 2083 of counter query 2053.

[0425] The mechanisms described above provide a means of creating,managing and executing counter queries that allows a designer to accesscounter data without specific knowledge of which hardware simulationmodels within batch simulation farm 1601 contain the desired designentity or entities. Furthermore, by accessing and storing count data bya parameter known to the designers, namely the design entitiescontaining the count event, the above described mechanisms provide asimple interface to access count data based solely on items known todesigners as part of the creation of count events. In addition, themeans described above provides for counter queries to be repeated andcount data to be returned at regular intervals without specificintervention from designers. In this manner, designers can provideon-going evaluation of count data without the burden of specificintervention to maintain the execution of queries.

[0426] In addition to obtaining design entity centric counter results,it would further be advantageous to provide a means of determiningtrends in counter instrumentation data within a batch simulation farmenvironment. In particular, it would be useful to determine differencesin rates of occurrence of count events, scaling appropriately for thenumber of simulation cycles executed, as simulation of one or moresimulation models, including instantiated instances of the count events,progresses over time.

[0427] To this end, and in accordance with an important feature of thepresent invention, instrumentation server 1699 implements a reportingmechanism that compares independently collected sets of count event datafor count events specified by a given user query and produces a reportshowing differences with respect to a predetermined threshold level,among the independently collected sets. This reporting mechanism willhereinafter be referred to as a “count difference analyzer”.

[0428] As implemented in accordance with the embodiments depictedherein, the count difference analyzer accepts as processing inputseither two basic counter output reports, similar in structure to basiccounter output report 2060, or two count data storage files among countdata storage files 2001 a-2001 n, and returns a count difference report.By comparing basic counter output reports, the count difference analyzerof the present invention determines changes in count results forinstrumentation count events present within the basic counter reports.The count difference analyzer described herein is particularly useful tocircuit designers to monitor instrumentation count event trends forcount events specified within a particular design entity or simulationmodel.

[0429] When performing relative comparisons between count data withincount data storage files 2001 a-2001 n, the count difference analyzer ofthe present invention provides an efficient means of count trendanalysis for all count events instantiated within an entire simulationmodel. This feature is particularly useful to simulation users whoseprimary interest is directed toward simulation results for simulationmodels as a whole.

[0430] Elements and processing steps required to implement a countdifference analyzer within instrumentation server 1699 are depicted inFIGS. 21A-21D. FIG. 21A depicts a system applicable within a batchsimulation farm for storing and accessing trends in count event data inaccordance with a preferred embodiment of the present invention and FIG.21C is a high-level flow diagram depicting steps performed within abatch simulation farm instrumentation server during count differenceanalysis performed within the system shown in FIG. 21A. The countdifference analysis process within the system depicted in FIG. 21Abegins as illustrated at step 2102 of FIG. 21C.

[0431] In accordance with the system illustrated in FIG. 21A, counterquery manager 2052 includes program instructions (not depicted) to spawninstances of a count difference analyzer engine (CDAE) 2100. CDAE 2100is a set of program instructions resident within memory 44 ofinstrumentation server 1699 that is spawned under two generalconditions. The first of these conditions occurs, as depicted at step2104 of FIG. 21C, in accordance with the status of an additional flagfield that is added to counter query 2053 (not depicted in FIG. 20H),which if asserted, initiates a count difference analysis to be performedin conjunction with counter query 2053. When this enable flag isasserted, counter query manager 2052 spawns CDAE 2100 at the conclusionof counter query processing as described hereinabove with reference toFIGS. 20A-20I.

[0432] As depicted at step 2120, CDAE 2100 compares the current mostrecently created basic counter output report 2060 b with the basiccounter output report 2060 a resulting from the previously most recentlyexecuted version the same counter query to produce a counter differencereport 2105 a (step 2122). As explained in further detail with referenceto FIG. 21D, counter difference report 2105 a contains informationindicating any relative changes in occurrences of one or more queriedcount events in accordance with disparities between basic counter outputreports 2060 a and 2060 b. A final counter difference report 2106 isdelivered to the user by the same means as those selected within reportdelivery field 2083 of query counter 2053 (FIG. 20H) for final querycounter report 2056 (FIG. 20F).

[0433] As illustrated at step 2106 of FIG. 21C, a second condition underwhich counter query manager 2052 spawns an instance of CDAE 2100 is atthe expiration of the predetermined counter query interval (e.g. at theend of each day) for every simulation model that has received counterdata for that day. As shown at step 2108 at a pre-specified counterquery interval, CDAE 2100 compares counter data storage files 2001 a and2001 b (possibly after conversion of the respective count data storagefiles into basic output counter report format) to produce a counterdifference report 2105 b (step 2122). In accordance with the embodimentdepicted in FIG. 21A, counter data storage files 2001 a and 2001 bconstitute the counter data for a particular simulation model oversequential time intervals (current and previous day, for example).Counter difference report 2105 b therefore includes data relating to therelative changes in count event occurrences for all count events in thegiven simulation model between the two days.

[0434] Counter difference reports for commissioned simulation models arestored by instrumentation server 1699 and are accessible by users via agraphical user interface 2107 that communicates with counter querymanager 2052. It should be noted that counter difference reports notinitiated by a counter query (i.e. those initiated in accordance withstep 2106) do not have a specified return destination. However,instrumentation server 1699 also provides a means, not described here indetail, that allows interested users to obtain counter differencereports for a certain model or models delivered in a manner similar tothat used to deliver final counter reports. The count differenceanalysis process terminates as depicted at step 2124 of FIG. 21C.

[0435] With reference to FIG. 21B in conjunction with FIG. 21D, thereare illustrated a system and a process for performing count differenceanalysis in accordance with a preferred embodiment of the presentinvention. The steps depicted in FIG. 21D provide a more detaileddescription of comparison and report generation steps 2120, 2108, and2122 of FIG. 21C.

[0436] The count difference analysis system illustrated in FIG. 21Bincludes CDAE 2100 receiving inputs from a pair of basic counter outputreports 2110 a and 2110 b for a given non-hierarchical counter query. Aresultant count difference report 2115 is generated by CDAE 2100 inaccordance with the process step described herein. As further depictedin FIG. 21B, basic counter output reports 2110 a and 2110 b includeindividual row-wise entries corresponding uniquely to a particularinstrumentation count event. Basic counter output report 2110 a providescount totals within count fields 2112 a for count events count1, count2,count3, count4, and count5, for simulation testcases performed over 9000simulation cycles as indicated within a cycle count field 2111 a.Likewise, basic count report 2110 b provides count totals within countfields 2112 b for count events count1, count2, count3, count4, andcount6, for simulation testcases performed over 18000 simulation cyclesas indicated within a cycle count field 2111 b.

[0437] The count difference analysis depicted in FIG. 21D begins at step2126 and continues with a normalization factor computation as shown atstep 2128. The normalization factor computation depicted at step 2128 isperformed by CDAE 2100 to normalize the count event count values withincount fields 2112 a and 2112 b to account for any difference in therespective number of simulation cycles (9000 and 18000, respectively)over which the results of basic counter output reports 2110 a and 2110 bwere obtained.

[0438] To normalize the count event values within count fields 2112 aand 2112 b, CDAE 2100 computes a ratio between cycle count fields 2111 band 2111 a and then multiplies the count values within count fields 2112a by this ratio. In the depicted embodiment, such normalization entailsmultiplying each of the count values constituting count fields 2112 a bythe ratio (18000/9000) before further processing.

[0439] Once the count values are normalized, CDAE 2100 determines thedifferences between the normalized count values. To this end, and asillustrated at step 2130 of FIG. 21D, CDAE 2100 first determines thosecount events that have either been added or removed in their entirety toor from basic count report 2110 a and 2110 b (i.e. appear in only one ofbasic counter reports 2110 a or 2110 b). Such added or removed countevents are reported in an event disparity report field 2113 of countdifference report 2115 (step 2132) and are removed from further counterdifference analysis processing.

[0440] Following the removal of data for count events that have beennewly added or removed and for which a trend analysis is thus currentlyimpracticable, CDAE 2100 identifies which of the remaining count eventsthat have a count value of zero as reported in either of basic counteroutput reports 2110 a or 2210 b (step 2134). Such zero-value countevents correspond to counts events that were present (i.e. not removedor added) within the design entities targeted by the counter queriesfrom which both basic counter output reports 2110 a and 2110 b wereproduced, but which have either just started occurring as reported inbasic counter output report 2110 b, or have occurred as reported inbasic counter output report 2110 a but not over the reporting intervalof basic counter output report 2110 b. Such zero-value count events arereported in a zero count event field 2114 of counter difference report2115 as depicted at step 2136 of FIG. 21D. As with the added or removedcount events recorded in event disparity field 2114, the identifiedzero-value count events are immaterial to trend analysis and are notfurther considered in the present counter difference analysis.

[0441] For remaining count events, which have non-zero count values andare not newly added or removed, CDAE 2100 computes the percentageincrease or decrease in occurrence between corresponding count eventsresults contained in basic counter output reports 2110 b and 2111 a asillustrated at step 2138. As depicted at steps 2140 and 2142, thosecount events whose percentage change exceeds a predetermined thresholdvalue (for example, plus or minus 20 percent in counter differencereport 2115), are reported in a threshold exceeded field 2116 withincounter difference report 2115 and are not further considered by CDAE2100. The percentage change threshold value can be specified by the userfor each query.

[0442] The remaining count events in count reports 2110 a and 2110 b,which have neither been added/removed nor exceeded the percentage changethreshold value constitute those count events whose count values, afterscaling, are within the specified percentage change threshold and arenot reported in counter difference report 2115.

[0443] In the foregoing description of FIGS. 21A and 21B, thefunctionality of CDAE 2100 has been described with respect to basiccounter query output reports. For cases in which CDAE 2100 must processcounter data storage files 2001 a-2001 n (as when comparing count datafor an entire simulation model) these counter data storage files arefirst converted to basic counter reports prior to commencing processingby CDAE 2100.

[0444] In accordance with a preferred embodiment of the presentinvention, a counter data storage file is converted to a basic counterreport by placing its cycle count field 2020 (FIG. 20C) value into cyclecount integer field 2061 (FIG. 20G) of the basic counter output report.Furthermore the count event list file for the object simulation model isutilized to generate count event identifier fields 2063 (FIG. 20G) ofthe basic count report. Finally, cumulative count values fields 2021a-2021 n (FIG. 20C) of the counter data storage file are utilized togenerate corresponding count value fields 2064 (FIG. 20G) within theresultant basic counter output report. In this manner, count datastorage files 2001 a-2001 n can be converted to basic counter reports ofthe form depicted in FIG. 20G suitable for processing by CDAE 2100.

[0445] With respect to FIGS. 21A-21D, the functionality of CDAE 2100 wasdescribed in terms of comparing count data storage files that containedcount data obtained on consecutive days or comparing basic counteroutput reports obtained from sequential executions of a given usercounter query. The extensions necessary for CDAE 2100 to compare countdata storage files obtained on non-consecutive days (separated by aweek, for example) or to compare basic counter output reports obtainedfrom non-sequential executions of a given counter query would be obviousto one skilled in the art and are included within the scope and spiritof the present invention. Furthermore, the present inventive conceptencompasses situations in which count data storage files containingcount data obtained over time intervals other than days (weeks, forexample), which could similarly be combined into two count data storagefiles and compared by CDAE 2001. Similar techniques could be applied tocombine basic count reports 2060 to produce reports spanning longerperiods of time that can be compared. In this manner, different timeperiods can be analyzed by CDAE 2100 to determine longer-term trends incounter instrumentation data. The above-described process provides ameans to analyze and report on longer term trends in counterinstrumentation data. This technique provides for automatic on-goinganalysis of counter instrumentation data for both user counter queryreports and for simulation models as a whole.

[0446] In a batch simulation farm environment, it would further beadvantageous to provide a means to collect and store, with minimalredundancy, those testcases in which harvest events, occur. Inaccordance with the embodiments depicted herein, testcases in whichharvest events occur are stored on a per simulation model basis, thusproviding a collection of testcases for the given simulation model thattrigger the harvest events incorporated within the instrumentationassociated with the simulation model. Each collection of such harvestevent triggering testcases are referred to herein as a “harvest testcasebucket” and are stored on a designated “harvest testcase server”.Similar to a failed testcase server, the harvest testcase server servesas a repository for testcases collected in response to the occurrence ofa harvest instrumentation event during execution of said testcasesduring simulation of said simulation model.

[0447] The foregoing description of count and fail event processing didnot account for multiple testcases being executed within a givensimulation run. In practice, however, multiple testcases are executedduring a given simulation job in order to amortize the overhead ofprocessing a simulation job over multiple testcases. When processingmultiple testcases during a simulation job, the simulation model isreset at the conclusion of a testcase before the execution of thesubsequent testcase.

[0448] For count and fail events, resetting the simulation modelincludes a mere reset of the event result registers between testcases.Therefore, between each testcase within a simulation job, counters 421(FIG. 4B) are reset to a value of zero and fail flags 424 (FIG. 4B) arecleared. The simple resetting of count and fail event results followingeach testcase is permissible as a consequence of the fact that thepractical significance of count and fail events may attach to, but doesnot extend beyond, the particular testcase in which they occur. This isin contrast to harvest events, whose significance lies simply in theconditions arising during any testcase that triggers the harvest eventduring simulation of a given simulation model.

[0449] Due to this singularity of significance of harvest events, it isdesirable to minimize redundancy in the collection of testcases in whicha given harvest event occurs during multi-testcase processing of asimulation model. The present invention provides a mechanism by whichthe processing of subsequent harvest-event-triggering testcases isinfluenced by previously recorded harvest events that have occurred inprevious testcases to prevent potentially extensive redundant harvesttestcase collection in the harvest testcase bucket.

[0450]FIG. 22A illustrates elements within batch simulation farm 1601utilized in collecting harvest event testcases in accordance with apreferred embodiment of the present invention. FIG. 22A illustrates thecontents of memory 44 within simulation client 1701 during the executionof the testcases within a simulation job. Prior to execution of thefirst testcase within a simulation job, RTX 1702, as part of step 1638of FIG. 16D, calls an API entry point init_harv( ) 2200. API entry point2200 is a routine that communicates with instrumentation server 1699and/or shared file system 1609 to obtain a list of harvest events from a“harvest hit table”, which have already occurred during simulationtesting of simulation model 1700. A separate, initially empty harvesthit table is stored for each commissioned simulation model withininstrumentation server 1699.

[0451] In response to obtaining a network copy of the harvest hit tablefor the current model, API entry point 2200 initializes a local harvesttable 2201 within simulator 1735. Local harvest hit table 2201 initiallyincludes a list of harvest event names that, as evidenced by theirinclusion within the harvest hit table referenced by API entry point2200, have already occurred during simulation testing of simulationmodel 1700.

[0452] In a manner analogous to that described with respect to API entrypoint disable_events( ) 1800 in FIG. 18B, API entry point init_harv( )2200 obtains a copy of the harvest hit table from one of two networksources. The first source is a master harvest hit table 2205 stored ondisk within instrumentation server 1699. The second possible source isan auxiliary harvest hit table 2207 that is stored in association withgeneral-purpose computer 1607 in shared file system 1609. Master harvesthit table 2205 serves as the primary (i.e. most currently updated)version of the harvest hit table. At regular intervals, instrumentationserver 1699 copies the contents of master harvest hit table 2205 intoauxiliary harvest hit table 2207, which serves as a backup copy of theharvest hit table.

[0453] The two-file system depicted in FIG. 22A is used to maintain theharvest hit table in a flexible and robust manner within geographicallydistributed batch simulation farm 1601 for reasons similar to thosediscussed above in connection with FIG. 18A. Simulation clients thatobtain the harvest hit table from shared file system 1609 canpotentially receive a “stale” copy of the harvest hit table. Althoughthis problem may be minimized by copying master file 2205 onto auxiliaryfile 2207 on a regular interval to quickly resolve any discrepanciesbetween master file 2205 and auxiliary file 2207, harvest testcaseprocessing based on a stale harvest hit table may result in redundancywithin the harvest testcase bucket. The undesirability of suchredundancy may, however, be outweighed by the additional robustnessgained by storing the harvest hit table at different network locations.The present invention includes techniques for removing redundanttestcases stored as a result of a stale harvest hit table, along withother sources of inconsistency, as described in further detail below.

[0454] After calling API entry point 2200, RTX 1702 executes the firsttestcase within the simulation job. Once the testcase completesexecution, and as part of step 1637 of FIG. 16D, RTX 1702 calls an APIentry point rpt_harv( ) 2202, which first examines harvest flags 423a-423 n (FIG. 4B) to determine which harvest events, if any, have beentriggered by the testcase. API entry point 2202 then compares theharvest event occurrences as recorded by the status of harvest flags 423a-423 n with the content of local harvest hit table 2201 to determine ifany preliminarily non-redundant harvest events (i.e. harvest eventstriggered during the testcase that do not match those recorded in localharvest hit table 2201) have occurred. In the absence of anypreliminarily non-redundant harvest events (i.e. all harvest eventstriggered during the testcase match those recorded in local harvest hittable 2201), API entry point 2202 terminates processing and returns toRTX 1702 an indication directing RTX 1702 not to copy the currenttestcase into a harvest testcase bucket 2300. By referring to localharvest hit table 2201, API entry point 2202 prevents unnecessarycommunication with instrumentation server 1699 in those cases in whichthe harvest events that were triggered by the current testcase havealready been collected during previous simulations of simulation model1700 within batch simulation farm 1601.

[0455] If, however, one or more preliminarily non-redundant harvestevents have occurred (i.e. at least one harvest event triggered by thetestcase does not match any harvest event recorded in local harvest hittable 2201), API entry point 2202 continues operation in one of twoalternative modes (described in further detail below) to potentiallyfurther validate the non-redundant status of the harvest event(s) inquestion. As explained in further detail below, a “direct mode” or an“indirect mode” of further non-redundant status inquiry may be pursuedby API entry point 2202. The direct non-redundant status inquiry isdesigned to ensure that only one testcase per harvest event is deliveredfrom a given simulation client for storage within harvest testcasebucket 2300. The indirect mode of non-redundant status inquiry resultsin the possibility that some redundant testcases (i.e. testcasestriggering the same harvest event) will be delivered from simulationclient 1701 to harvest testcase bucket 2300.

[0456] It should be noted that a preliminarily non-redundant harvestevent, as recorded within local harvest hit table 2201, may in fact beredundant with respect to interim testcase activity within batchsimulation farm 1601. In the time interval between the initialization oflocal harvest hit table 2201 and the completion of the object testcase,another simulation client may detect and record the occurrence of one ormore of the preliminarily non-redundant harvest events detected bysimulator 1735. In this case, harvest events appearing to be new inaccordance with local harvest hit table 2201, have actually alreadyoccurred during another testcase. In accordance with a preferredembodiment of the present invention, API entry point rpt_hrv( ) 2202 mayundertake additional processing steps to ensure that an apparently newlyoccurring harvest event has not actually occurred elsewhere within batchsimulation farm 1601.

[0457] In the direct mode of non-redundancy status inquiry, API entrypoint 2202 opens a direct network connection (e.g. Unix socketconnection) on network 1720 to a harvest manager program 2215, whichexecutes on instrumentation server 1699. Over this direct networkconnection, API entry point rpt_hrv( ) 2202 delivers an aggregateinstrumentation data packet consisting of the contents of harvest cyclecounters 422 a-422 n, harvest flags 423 a-423 n, and the name of thecurrent testcase.

[0458] Harvest manager program 2215 first verifies that the aggregateinstrumentation data packet is associated with a simulation modelcommissioned within instrumentation server 1699 in accordance with themeans described with respect to FIGS. 17A-17C. Following packetcommissioning verification, harvest manager program 2215 then comparesthe contents of master harvest hit table 2205 with the contents of thereceived aggregate instrumentation data packet to determine if anypreliminarily non-redundant harvest event occurrences are recorded inthe master harvest hit table 2205.

[0459] Any of the apparently new harvest events that are not currentlyrecorded in master harvest hit table 2205, are recorded by harvestmanager program 2215 in master harvest hit table 2205 in associationwith the name of the current testcase. If multiple new harvest eventshave occurred in the current testcase, each of these events is recordedas having been triggered by the current testcase. In this manner, it ispossible to determine which specific harvest events have been triggeredby each testcase within harvest testcase bucket 2300. Subsequent todetecting and recording newly occurring harvest events in master harvesthit table 2205, harvest manager program 2215 returns an indication, overthe direct network connection on network 1720, to API entry point 2202to deliver a copy of the current testcase to harvest testcase bucket2300.

[0460] In the case that all of the apparently new harvest events (i.e.all harvest events triggered during the current testcase having nocorresponding entry in local harvest hit table 2201) are currentlyrecorded in master harvest hit table 2205, harvest manager program 2215returns an indication to API entry point 2202 over the direct networkconnection on network 1720, directing API entry point 2202 not todeliver a copy of the current testcase.

[0461] In accordance with an important feature of the depictedembodiment, only one simulation client may have an active networkconnection with harvest manager 2215 at any given time. Prior toobtaining a communicative connection with harvest manager program 2215,other simulation clients attempting to validate the redundancy status ofharvest events must wait until harvest manager program 2215 has firstcompleted processing a current connection, and second has performed anynecessary updates to master harvest hit table 2205.

[0462] In this manner, updates to master harvest hit table 2205 areserialized, thereby preventing the collection of redundant testcases forany given harvest event. Therefore, in direct non-redundancyverification mode, once a testcase is collected within harvest testcasebucket 2300 in association with the recordation of a given harvest eventin master harvest hit table 2205, no further testcases will be collectedin association with the same harvest event.

[0463] While direct non-redundancy verification mode prevents thecollection of multiple, effectively redundant testcases for a givenharvest event, it can potentially be error prone and costly in practice.The direct network connection between simulation client 1701 andinstrumentation server 1699 maybe costly to establish, subject to errorsin high network traffic conditions, and serves only one simulationclient at a time. These issues are especially relevant for simulationclients residing in geographically distant nodes. In such circumstances,it maybe preferable to permit a certain level of redundancy in harvesttestcase bucket 2300 in order to reduce the processing and communicationoverhead required for direct non-redundancy inquiries.

[0464] To this end, an indirect non-redundancy verification inquiry isutilized wherein API entry point 2202 uses only local harvest hit table2201 in determining whether or not to collect a testcase. In indirectnon-redundancy verification, API entry point rpt_hrv( ) 2202 bypassesthe step of further validating an apparently new harvest event withharvest manager 2215. Communication overhead associated with a directnetwork connection with harvest manager program 2215 is thus reduced atthe cost of potential redundancy in harvest testcase bucket 2300.

[0465] In indirect harvest mode, when, in accordance with the content oflocal harvest hit table 2201, an apparently new harvest event has beentriggered by a current testcase, API entry point 2202 prepares anaggregate instrumentation data packet containing the contents of harvestcycle counters 422 a-422 n, harvest flags 423 a-423 n, and the name ofthe current testcase.

[0466] This aggregate instrumentation data packet is delivered toinstrumentation server 1699 over network 1720 utilizing a non-directnetwork connection. Such a non-direct network connection may beestablished via several well-known communications mechanisms such ascustom file transfer or messaging protocols. This communicationmechanism accepts the aggregate instrumentation data packet for deliveryand subsequently returns control to API entry point 2202. The aggregateinstrumentation data packet is then delivered to harvest manager program2215 at a later time independent of the subsequent execution of APIentry point 2202 or RTX 1702. This communication mechanism allowsmultiple simulation clients to be served simultaneously in a batchfashion, and does not require a direct network connection per simulationclient to be established for harvest manager program 2215.

[0467] This is in contrast to the communication means utilized during adirect non-redundancy verification inquiry, wherein API entry point 2202cannot return control to RTX 1702 until the aggregate instrumentationpacket has been delivered to instrumentation server 1699, processing ofthe packet has been completed, and an indication has been returned byharvest manager program 2215 of whether or not to copy the currenttestcase to harvest testcase bucket 2300. Simulation clients mustsequentially execute these collective steps to gain access toinstrumentation server 1699. This can lead to bottlenecks in performancein heavy network load situations, especially in large geographicallydistributed batch simulation farms.

[0468] When harvest manager program 2215 receives an aggregateinstrumentation data packet from a simulation client operating inindirect non-redundancy verification mode, the commissioning status ofthe aggregate data packet is first validated according to the meansdescribed above in conjunction with FIGS. 17A-C. The contents of theaggregate data packet are compared to master harvest hit table 2205 in amanner analogous to that utilized in direct harvest mode. Any harvestevents recorded in the aggregate instrumentation data packet that arenot currently included within master harvest hit table 2205 areidentified and recorded in master harvest hit table 2205. If all harvestevents present in the aggregate instrumentation data packet are alreadyrecorded in master harvest hit table 2205, the aggregate instrumentationdata packet is discarded.

[0469] As in direct non-redundancy verification mode, in indirectharvest mode, harvest manager program 2215 must processes aggregateinstrumentation data packets received from clients in a serial fashion.In this manner, accesses to master harvest hit table 2205 for simulationclients utilizing indirect non-redundancy verification mode are alsoserialized and only one testcase is recorded in master harvest hit table2205 as having triggered each harvest event.

[0470] However, in indirect non-redundancy verification mode, it ispossible for multiple testcases to be delivered from simulation clientsto harvest testcase bucket 2300 for the same harvest event. As oneexample, two simulation clients may receive the same harvest hit tablecontent from the init_harv( ) call by API entry point 2200. Thesesimulation clients then independently execute differing testcases thatboth trigger the same, preliminarily non-redundant (in accordance withtheir respective local harvest hit tables) harvest event. In bothsimulation clients, API entry point rpt_hrv( ) 2202 will instruct RTX1702 to harvest their respective testcases.

[0471] Furthermore, aggregate instrumentation data packets for bothtestcases will be delivered to harvest data manager program 2215. Theaggregate instrumentation data packet received first will be recorded inmaster harvest hit table 2205. The subsequently received aggregateinstrumentation data packet will be ignored due to the fact that theobject harvest event has already been recorded in master harvest hittable 2205. In this situation, the testcase reported by the simulationclient producing the second aggregate instrumentation data packet isredundantly stored within harvest testcase bucket 2300. As explained infurther detail with reference to FIGS. 23A-23C, the present inventionprovides techniques for removing these redundant testcases from theharvest testcase bucket 2300.

[0472] In a preferred embodiment, harvest manager program 2215 isconfigured to simultaneously process aggregate instrumentation datapackets from simulation clients operating in either direct or indirectnon-redundancy verification mode. Therefore, simulation clients within abatch simulation farm may operate in either direct or indirect harvestmode, according to which mode is most advantageous.

[0473] As a next step in processing in either direct or indirect mode,API entry point 2202 updates local harvest hit table 2201 to recordthose harvest events that were triggered by the current testcase. Inthis manner, subsequent testcases within the simulation job will beprevented from potentially erroneously harvesting another testcase for aharvest event that has already occurred.

[0474] As an additional optional additional processing step available ineither direct or indirect harvest mode, API entry point 2202 may alsocommunicate with instrumentation server 1699 and/or shared file system1609 to obtain an updated copy of the harvest hit table. Typically, thisdata is obtained from shared file system 1609 to reduce thecommunication load on instrumentation server 1699.

[0475] The harvest hit table information is used to further update localharvest hit table 2201 with a more current image of those harvest eventsthat have been detected and recorded during simulation jobs executing onother simulation clients. In this manner, the processing of harvestevents by simulation client 1701 is influenced by harvest events alreadyharvested by other simulation clients since the last update of localharvest hit table 2201, and unnecessary network communication is avoidedwhile processing subsequent testcases that hit harvest events capturedby other simulation clients. This additional processing step is mostadvantageous in circumstances in which testcases take a long period oftime to complete, and therefore many additional harvest events may havebeen detected and recorded in parallel on other simulation clients.

[0476] Following data packet processing by harvest manager program 2215,API routine 2202 returns an indication, received either directly fromharvest manager program 2215 in direct mode or by an examination of datastructure 2201 in indirect mode, to RTX 1702 of whether or not thecurrent testcase is to be copied to harvest testcase bucket 2300. Uponreceiving an indication to save the current testcase from API entrypoint 2202, RTX 1702 delivers a copy of current testcase to harvesttestcase server 2210.

[0477] Each of a set of harvested testcases 2213 a-2213 n is stored inassociation with a particular simulation model on a disk storage device2211 associated with harvest testcase server 2210. Harvest testcaseserver 2210 also maintains a harvested testcase list 2214 that includesthe name of each testcase within harvest testcase bucket 2300. Harvestedtestcase list 2214 is updated whenever a new testcase is stored toprovide an up-to-date list of the testcase names for the harvesttestcases stored for the given simulation model.

[0478] It should be noted that certain errors can occur that prevent RTX1702 from successfully storing the current testcase on harvest testcaseserver 2210. However, when such an error occurs in direct non-redundancyverification mode, master harvest hit table 2205 has already beenupdated to indicate that the current testcase has been collected.Similarly, in indirect mode, master harvest hit table 2205 has been orwill be similarly updated (barring errors in processing the aggregateinstrumentation data packet sent to instrumentation server 1699 duringstep 2256 of FIG. 22B). Such failures to store the testcase can cause aninconsistency between master harvest hit table 2205 and harvest testcasebucket 2300 stored on instrumentation server 2210.

[0479] After the testcase has been harvested if necessary, RTX 1702calls API entry point clr_harv( ) 2203. API entry point 2203 clearsharvest flags 423 a-423 n in preparation to run a subsequent testcase.RTX 1702 then executes the next testcase within the simulation job,repeating the harvest testcase verification and collection process untilall testcases within the simulation job have been completed.

[0480]FIG. 22B is a flow diagram depicting in greater detail theoperation of API entry point 2202 in accordance with a preferredembodiment of the present invention. API entry point 2202 beginsexecution at step 2250 upon being called by RTX 1702. The processcontinues at step 2251 which depicts a comparison between the contentsof harvest flags 423 a-423 n with the content of local harvest hit table2201 to determine if any apparently new harvest events have beentriggered by the current testcase (step 2252).

[0481] If, in accordance with the comparison shown at step 2251, noapparently new harvest events have occurred, the process continues asillustrated at step 2258, with the setting of an internal indicationinstructing RTX 1702 not to harvest the current testcase. Otherwise, asdepicted at step 2253, an aggregate instrumentation data packet isgenerated containing the contents of harvest cycle counters 422 a-422 n,harvest flags 423 a-423 n, and the name of the current testcase.

[0482] Proceeding to step 2254, wherein is depicted a determination ofwhether non-redundancy verification is to be performed in direct orindirect mode. If indirect mode is selected, and as illustrated at step2256, the aggregate instrumentation packet generated in step 2253 isscheduled for a later delivery to harvest manager program 2215. Ifdirect non-redundancy verification processing is selected by thesimulation client, API routine rpt_hrv( ) 2202 continues as shown atstep 2255, by delivering the aggregate instrumentation data packet toharvest manager program 2215 via a direct network connection overnetwork 1720 to validate the first occurrence status of the apparentlynew harvest events (step 2257).

[0483] As part of step 2257 a determination is returned to API entrypoint 2202 from harvest manager program 2215 of whether the harvestevents are actually new. If the harvest events are not new (i.e. arerecorded in the current network harvest hit table), the processcontinues as depicted at step 2258, with API entry point 2202 setting aninternal indication instructing RTX 1702 not to copy the currenttestcase to harvest testcase bucket 2300. If, as determined by thecomparison of aggregate instrumentation packet data with the networkharvest hit table, the harvest events are actually new, API entry point2202 sets an internal indication instructing RTX 1702 to copy anddeliver the current testcase to harvest testcase bucket 2300 (step2259).

[0484] API entry point 2202 processing continues at step 2260, with anupdate of local harvest hit table 2201 to include those harvest eventsthat have occurred in the current testcase. Continuing at step 2261, adetermination is made of whether or not local harvest hit table 2201should be updated to reflect those harvest events captured by othersimulation clients. If the optional update is selected, API routine 2202requests an updated image of the harvest hit table from eitherinstrumentation server 1699 and/or shared file system 1609 asillustrated at step 2263. Next, as depicted at step 2264 local harvesthit table 2201 is further updated to reflect those additional harvestevents obtained at step 2263. Processing terminates at step 2265,depicting the return of the internal indication of whether or not toharvest the current testcase set by API entry point 2202 at either steps2255 or 2256 to RTX 1702.

[0485] With reference to FIG. 22C, there is depicted a flow diagramillustrating in greater detail the operation of harvest manager program2215 in accordance with a preferred embodiment of the present invention.Harvest manager program 2215 begins processing at step 2280 andcontinues as illustrated at step 2281, with a determination of whether arequest for a direct network connection over network 1720 from asimulation client operating in direct non-redundancy verification modeis present.

[0486] If so, and as illustrated at step 2282, harvest manager program2215 receives the aggregate instrumentation data packet transmitted overthe direct network connection on network 1720 as a part of step 2255 ofFIG. 22B. The process continues at step 2283, with harvest managerprogram 2215 comparing the contents of the aggregate instrumentationdata packet to master harvest hit table 2205 to determine if anyactually new harvest events have occurred and updating, as necessary,master harvest hit table 2205 to record these new events (step 2283).

[0487] Proceeding to step 2284, harvest manager program 2215 determinesif any new harvest events have been recorded at step 2283. If newharvest events have been recorded at step 2283, harvest manager program2215 returns an indication through the direct network connection onnetwork 1720 instructing the simulation client to copy the currenttestcase into harvest testcase bucket 2300 as depicted at step 2285. Ifno new harvest events are recorded at step 2283, an indication isdelivered from harvest manager program 2215 through the direct networkconnection on network 1720 instructing the simulation client not tocollect the current testcase within harvest testcase bucket 2300 asdepicted at step 2286.

[0488] The process continues at step 2287, which illustrates adetermination of whether an aggregate instrumentation data packet from asimulation client operating in indirect non-redundancy verification modehas been delivered from simulation client 1701. If an aggregateinstrumentation data packet has been delivered, the process continues asillustrated at step 2288, with harvest manager program 2215 receivingthe aggregate instrumentation data packet delivered to harvest managerprogram 2215 as a part of step 2256 of FIG. 22B. Continuing at step2289, harvest manager program 2215 compares the contents of theaggregate instrumentation data packet to master harvest hit table 2205to determine if any actually new harvest events have occurred andupdating, as necessary, master harvest hit table 2205 to record thesenew events.

[0489] Next, as depicted at step 2330 harvest manager program 2215undertakes processing steps required to resolve inconsistencies betweenthe testcases collected within harvest testcase bucket 2300 and thetestcases recorded within master harvest hit table 2215. The particularprocessing steps performed during step 2330 are explained in furtherdetail with reference to FIGS. 23A-23C. In this manner, harvest managerprogram 2215 processes aggregate instrumentation data packets receivedfrom simulation clients in both direct and indirect harvest mode. Theprocess returns to step 2281 to repeat for each direct or indirectnon-redundancy inquiry delivered by the simulation client. The abovedescribed mechanism for harvesting testcases provides for a robust andefficient means to collect testcases, minimizing duplication wherepossible, that exercise harvest events in batch simulation farm 1601.

[0490] The mechanism for harvesting testcases described with referenceto FIGS. 22A-22C has two main sources of inconsistency between theentries within master harvest hit table 2205 and the testcases recordedin harvest testcase bucket 2300. The first of these inconsistencies,referred to hereafter as a “lost harvest testcase”, occurs when aharvest event is recorded in master harvest hit table 2205 inassociation with a particular testcase, but RTX 1702 fails tosuccessfully store the testcase on harvest testcase server 2210. In sucha circumstance, master harvest hit table 2205 includes a harvest evententry for a testcase that is not actually stored within harvest testcasebucket 2300. As such, subsequent testcases that trigger the objectharvest event will not be collected within harvest testcase bucket 2300.Left uncorrected, this condition prevents any future collection oftestcases that trigger harvest events whose recordation within masterharvest hit table 2205 has become effectively erroneous.

[0491] The second source of inconsistency between the entries withinmaster harvest hit table 2205 and the testcases recorded in harvesttestcase bucket 2300, referred to hereafter as an “extraneous harvesttestcase”, may occur as a result of the nature of the previouslydescribed indirect redundancy verification mode optionally undertaken byAPI entry point rpt_hrv( ) 2202. As previously explained with referenceto FIGS. 21A-21C, an indirect non-redundancy status inquiry results inthe possibility that testcases which are redundant with respect to agiven harvest event may be collected in association with a givensimulation model within harvest testcase bucket 2300. This is despitethe fact that only one testcase is recorded per harvest event in masterharvest hit table 2205. The collection of potentially thousands ofextraneous testcases within harvest testcase bucket 2300 is inherentlyundesirable. Furthermore, it is not possible to definitively determinefrom master harvest hit table 2205 which harvest events are triggered bythese extraneous testcases.

[0492] It would therefore be advantageous to provide a means, hereafterreferred to as “harvest annealing”, to resolve these inconsistencies.Referring to FIG. 23A, there are depicted additional elements withininstrumentation server 1699 and harvest testcase server 2210 that areutilized in resolving inconsistencies between master harvest hit table2205 and harvest testcase bucket 2300.

[0493] As illustrated in FIG. 23A, the contents of memory 44 withinharvest testcase server 2210 and instrumentation server 1699 utilized toimplement harvest annealing include a harvest annealing program 2305 andharvest manager program 2215, respectively. At proscribed intervals,harvest testcase server 2210 initiates harvest annealing program 2305for a given simulation model. Harvest annealing program 2305 first opensa direct network connection on network 1720 to harvest manager program2215 executing on instrumentation server 1699. Harvest annealing program2305 delivers harvest testcase list 2214, which contains a list of thenames of all testcases stored on harvest testcase server 2210 for thegiven model, to harvest manager program 2215.

[0494] Harvest manager program 2215 compares the testcase name fieldswithin master harvest hit table 2205 to the entries of testcase list2214. Any entry in master harvest hit table 2215 whose testcase fielddoes not correspond to any testcase name entry within harvest testcaselist 2214 indicates a lost harvest testcase that is not present inharvest testcase bucket 2300. Detection of such lost harvest testcasesresults in the removal of the corresponding harvest event entries frommaster harvest hit table 2205. Removal of these harvest event entriesfrom master harvest hit table 2205 enables collection of testcasestriggering the object harvest events during future simulation jobs.

[0495] As a further step in the comparison, harvest manager program 2215produces a list of each testcase recorded in harvest testcase list 2214which cannot be correlated with the testcase field of an entry in masterharvest hit table 2205. These testcases correspond to the extraneousharvest testcases described above. This list of extraneous testcases2302 is returned to harvest annealing program 2305 over the directnetwork connection on network 1720. Harvest annealing program 2305removes the extraneous testcases indicated in the extraneous testcaselist from harvest testcase bucket 2300.

[0496]FIG. 23B provides a more detailed illustration of the datastructure and content of harvest testcase list 2214 and master harvesthit table 2205 as they exist prior to the harvest annealing process ofthe present invention. Within harvest testcase list 2214, a name field2360 includes data field entries containing the names of testcases,test1, test2, test3, test4, and test5, which are stored on harvesttestcase server 2010 in association with simulation model 1700.

[0497] Master harvest hit table 2205 includes row-wise entries forharvest events that have been recorded by simulation clients in thecourse of simulation on batch simulation farm 1601. Each harvest evententry within master harvest hit table 2205 includes an extended eventidentifier field 2362 that contains the name of the harvest event. Eachentry also includes a testcase name field 2363 that contains the name ofthe testcase that triggered the corresponding harvest event. An instanceidentifier field 2361 is also included within each entry to provide anindication of the specific instance of the harvest event that wastriggered. Harvest events may be collected in either a hierarchical ornon-hierarchical mode. In a hierarchical mode, each instance of a givenharvest event is processed independently, and a separate testcaseexercising each instance of the harvest event may be collectedaccordingly. In such a case, when determining if a reported harvestevent is new, harvest manager program 2215 considers instance identifierfield 2361.

[0498] In non-hierarchical mode, harvest events are collected in harvesttestcase bucket 2300 without regard to each specific instance of a givenharvest event. Once a testcase exercising a particular instance of aharvest event is detected and recorded, no further testcases arecollected for that event. To this end, instance identifier field 2361 isignored by harvest manager program 2215 when operating innon-hierarchical harvest mode.

[0499] Regardless of the harvest annealing mode selected by harvestmanager program 2215, instance identifier field 2361 is present inmaster harvest hit table 2205. In either mode, instance identifier field2361 maintains the potential utility in determining which specificinstance of the harvest event has been triggered by a given testcase.

[0500] In comparing the exemplary entries within harvest testcase list2214 with the entries in master harvest hit table 2205, a “losttestcase” entry 2264 contains a harvest event entry (i.e. D B5 L E4)corresponding to a testcase (i.e. test7) that is not included among thetestcase names contained in harvest testcase list 2214. Responsive tothe harvest annealing file comparison for the exemplary files depictedFIG. 23B, entry 2264 is removed from master harvest hit table 2205.

[0501] As further depicted in FIG. 23B, harvest testcase list 2214includes two entries, test2 and test5, which are not included within anyentry within test case name field 2363 of master harvest hit table 2205.As previously explained with reference to FIG. 23A, such extraneoustestcase entries will be removed from harvest testcase bucket 2300 andharvest testcase list 2214 within harvest testcase server 2210.

[0502] Referring to FIG. 23C, there is depicted a flow diagramillustrating in further detail the steps performed by harvest managerprogram 2215 during step 2330 of FIG. 22C to process annealing requestsfrom harvest testcase server 2210. The process begins at step 2370 andproceeds to step 2371, which depicts harvest manager program 2215receiving harvest testcase list 2214 from harvest testcase server 2210.Next, as illustrated at step 2372, harvest manager program 2215sequentially examines the entries within harvest testcase list 2214 andmarks an internal flag (not depicted in FIG. 23B) in master harvest hittable 2205 for each entry whose testcase name field 2363 corresponds toan entry in harvest testcase list 2214.

[0503] At the conclusion of the examination of testcase list 2214, anyunmarked entries in master harvest hit table 2205 correspond to the setof lost testcase inconsistencies. In addition, while sequencing throughharvest testcase list 2214, harvest manager program 2215 creates anextraneous testcase list containing testcase names that have nocorresponding entry in master harvest hit table 2205.

[0504] The process continues as illustrated at step 2734, which depictsmaster harvest program 2215 removing the unmarked entries from masterharvest hit table 2205. Proceeding as depicted at step 2375, harvestmanager program 2215 returns the extraneous testcase list to harvesttestcase server 2210 over the direct network connection on network 1720.Harvest testcase server 2010 will, at a later time, remove theseextraneous testcases from harvest testcase bucket 2300 and harvesttestcase list 2214. The harvest annealing process then terminates asdepicted at step 2376. The above-described process provides a means bywhich to resolve inconsistencies arising in the process of harvestingtestcases in a geographically distributed batch simulation farm 1601.

[0505] While the invention has been particularly shown as described withreference to a preferred embodiment, it will be understood by thoseskilled in the art that various changes in form and detail may be madetherein without departing from the spirit and scope of the invention.One of the embodiments of the invention can be implemented as sets ofinstructions resident in random access memory 28 of one or more computersystems configured generally as described in FIG. 1 and FIG. 2. Untilrequired by computer system 10, the set of instructions may be stored inanother computer readable storage device, such as disk drive 33 or in aremovable storage device such as an optical disk for eventual use in aCD-ROM drive or a floppy disk for eventual use in a floppy disk drive.The set of instructions may be referred to as a computer programproduct. Further, the set of instructions can be stored in the memory ofanother computer and transmitted over a local area network or a widearea network, such as the Internet, when desired by the user. It istherefore contemplated that such modifications can be made withoutdeparting from the spirit or scope of the present invention as definedin the appended claims.

What is claimed is:
 1. A method for tracking frequently occurring failevents that are detected during testcase simulation of a simulationmodel within a batch simulation farm wherein testcases are executedwithin respect to a simulation model on one or more simulation clients,said method comprising: within an instrumentation server: receiving failevent packets from said simulation clients, wherein said fail eventpackets contains an aggregate of detected occurrences of a specifiedfail event; and monitoring the rate of occurrence of said specified failevent from received fail event packets to detect an excess rate ofoccurrence of said specified fail event.
 2. The method of claim 1,further comprising: within said instrumentation server: maintaining acounter that specifies the rate of occurrences of said specified failevent; reading said received aggregate fail event packet; and responsiveto said aggregate fail event packet including a recorded occurrence ofsaid specified fail event, incrementing said counter.
 3. The method ofclaim 2, further comprising decrementing said counter at a predeterminedtime interval such that a rate of occurrence of said specified failevent may be determined within said instrumentation server.
 4. Themethod of claim 1, further comprising: within said instrumentationserver: comparing the rate of occurrence of said specified fail eventwith a predetermined threshold rate; and responsive to the rate ofoccurrence of said specified fail event exceeding said predeterminedthreshold rate, adding said specified fail event to a fail event disablelist.
 5. The method of claim 4, further comprising: prior to testcasesimulation of said simulation model within said one or more simulationclients: retrieving said fail event disable list from saidinstrumentation server; and disabling fail events specified within saidinstrumentation event disable list.
 6. The method of claim 1, whereinsaid monitoring the rate of occurrence of said specified fail event ispreceded by: delivering an instrumentation eventlist from saidsimulation client to said instrumentation server, wherein said eventlistcontains instrumentation event information for said simulation model;and within said instrumentation server: computing a digital signaturethat uniquely identifies contents of said instrumentation eventlist asbeing associated with said simulation model; and responsive to receivingsimulation data from said simulation client, utilizing said digitalsignature to associate said simulation data with said simulation model.7. The method of claim 6, wherein said instrumentation server computessaid digital signature utilizing a cyclic redundancy check algorithm,said method further comprising computing a digital signature within saidsimulation client utilizing said cyclic redundancy check algorithm. 8.The method of claim 7, wherein said monitoring the rate of occurrence ofsaid specified fail event is preceded by: responsive to receiving saidaggregate fail event packet within said instrumentation server:comparing the digital signature contained in said aggregateinstrumentation packet with the digital signature computed by saidinstrumentation server to determine whether or not a match exists;responsive the digital signature contained in said aggregate fail eventpacket matching the digital signature computed by said instrumentationserver, processing said aggregate fail event packet within saidinstrumentation server; and responsive to the digital signaturecontained in said aggregate instrumentation packet not matching thedigital signature computed by said instrumentation server, discardingsaid aggregate fail event packet.
 9. A system for tracking frequentlyoccurring fail events that are detected during test case simulation of asimulation model within a batch simulation farm wherein testcases areexecuted within respect to a simulation model on one or more simulationclients, said system comprising: an instrumentation server includingprocessing means for: receiving fail event packets from said simulationclients, wherein said fail event packets contains an aggregate ofdetected occurrences of a specified fail event; and monitoring the rateof occurrence of said specified fail event from received fail eventpackets to detect an excess rate of occurrence of said specified failevent.
 10. The system of claim 9, wherein said instrumentation serverfurther comprises: a counter that specifies the rate of occurrences ofsaid specified fail event; processing means for reading said receivedaggregate fail event packet; and processing means responsive to saidaggregate fail event packet including a recorded occurrence of saidspecified fail event for incrementing said counter.
 11. The system ofclaim 10, further comprising processing means for decrementing saidcounter at a predetermined time interval such that a rate of occurrenceof said specified fail event may be determined within saidinstrumentation server.
 12. The system of claim 9, wherein saidinstrumentation server further comprises: processing means for comparingthe rate of occurrence of said specified fail event with a predeterminedthreshold rate; and processing means responsive to the rate ofoccurrence of said specified fail event exceeding said predeterminedthreshold rate for adding said specified fail event to a fail eventdisable list.
 13. The system of claim 12, further comprising: processingmeans for retrieving said fail event disable list from saidinstrumentation server; and processing means for disabling fail eventsspecified within said instrumentation event disable list.
 14. The systemof claim 9, further comprising: processing means for delivering aninstrumentation eventlist from said simulation client to saidinstrumentation server, wherein said eventlist contains instrumentationevent information for said simulation model; and within saidinstrumentation server: processing means for computing a digitalsignature that uniquely identifies contents of said instrumentationeventlist as being associated with said simulation model; and processingmeans responsive to receiving simulation data from said simulationclient for utilizing said digital signature to associate said simulationdata with said simulation model.
 15. The system of claim 14, whereinsaid instrumentation server computes said digital signature utilizing acyclic redundancy check algorithm, said system further comprisingprocessing means for computing a digital signature within saidsimulation client utilizing said cyclic redundancy check algorithm. 16.The system of claim 15, further comprising: processing means responsiveto receiving said aggregate fail event packet within saidinstrumentation server for: comparing the digital signature contained insaid aggregate instrumentation packet with the digital signaturecomputed by said instrumentation server to determine whether or not amatch exists; responsive the digital signature contained in saidaggregate fail event packet matching the digital signature computed bysaid instrumentation server, processing said aggregate fail event packetwithin said instrumentation server; and responsive to the digitalsignature contained in said aggregate instrumentation packet notmatching the digital signature computed by said instrumentation server,discarding said aggregate fail event packet.
 17. A computer programproduct for tracking frequently occurring fail events that are detectedduring test case simulation of a simulation model within a batchsimulation farm wherein testcases are executed within respect to asimulation model on one or more simulation clients, said computerprogram product comprising: program instruction means for receiving failevent packets from said simulation clients, wherein said fail eventpackets contains an aggregate of detected occurrences of a specifiedfail event; and program instruction means for monitoring the rate ofoccurrence of said specified fail event from received fail event packetsto detect an excess rate of occurrence of said specified fail event. 18.The computer program product of claim 17, further comprising: programinstruction means for implementing a counter that specifies the rate ofoccurrences of said specified fail event; program instruction means forreading said received aggregate fail event packet; and programinstruction means responsive to said aggregate fail event packetincluding a recorded occurrence of said specified fail event forincrementing said counter.
 19. The computer program product of claim 18,further comprising program instruction means for decrementing saidcounter at a predetermined time interval such that a rate of occurrenceof said specified fail event may be determined within saidinstrumentation server.
 20. The computer program product of claim 17,further comprising: program instruction means for comparing the rate ofoccurrence of said specified fail event with a predetermined thresholdrate; and program instruction means responsive to the rate of occurrenceof said specified fail event exceeding said predetermined threshold ratefor adding said specified fail event to a fail event disable list. 21.The computer program product of claim 20, further comprising: programinstruction means for retrieving said fail event disable list from saidinstrumentation server; and program instruction means for disabling failevents specified within said instrumentation event disable list.
 22. Thecomputer program product of claim 17, further comprising: programinstruction means for delivering an instrumentation eventlist from saidsimulation client to said instrumentation server, wherein said eventlistcontains instrumentation event information for said simulation model;program instruction means for computing a digital signature thatuniquely identifies contents of said instrumentation eventlist as beingassociated with said simulation model; and program instruction meansresponsive to receiving simulation data from said simulation client forutilizing said digital signature to associate said simulation data withsaid simulation model.
 23. The computer program product of claim 22,wherein said instrumentation server computes said digital signatureutilizing a cyclic redundancy check algorithm, said computer programproduct further comprising program instruction means for computing adigital signature within said simulation client utilizing said cyclicredundancy check algorithm.
 24. The computer program product of claim23, further comprising: program instruction means responsive toreceiving said aggregate fail event packet within said instrumentationserver for: comparing the digital signature contained in said aggregateinstrumentation packet with the digital signature computed by saidinstrumentation server to determine whether or not a match exists;responsive the digital signature contained in said aggregate fail eventpacket matching the digital signature computed by said instrumentationserver, processing said aggregate fail event packet within saidinstrumentation server; and responsive to the digital signaturecontained in said aggregate instrumentation packet not matching thedigital signature computed by said instrumentation server, discardingsaid aggregate fail event packet.